From c1fc42012edb4a6bc6c6a41b8ab6a4785aa7013c Mon Sep 17 00:00:00 2001
From: alessandrozocca <alessandrozocca@users.noreply.github.com>
Date: Mon, 23 Oct 2023 15:34:10 +0000
Subject: [PATCH] deploy: c9b11e0c271a56842aa510905457538551d48259

---
 ...aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png | Bin 59237 -> 0 bytes
 ...52107607ce6477043c6d88d4b24d99edb81d67.png | Bin 19954 -> 0 bytes
 ...a5feeb881571ee6c18223cc66eddc73637a204.png | Bin 0 -> 55703 bytes
 ...5174e177a3d404163772d73bf74d8d1f58eadb.svg | 191 +++++
 ...a3214cbfa7081877adee3b8ad89bf91985bca.png} | Bin 13607 -> 13607 bytes
 ...3d4b21d0cca066c8db29f83ae3012feadff2c.png} | Bin 13120 -> 13120 bytes
 ...69f66c2c62f3b23009b4e8b66c8d16b7313ae3.png | Bin 0 -> 20271 bytes
 ...ae86ce571d705bbaf85dfdeb4f2c3e47c85eb.png} | Bin 14298 -> 14298 bytes
 _images/seating_flow_model.png                | Bin 387792 -> 0 bytes
 _images/seating_model_basic.png               | Bin 668506 -> 0 bytes
 .../notebooks/04/gasoline-distribution.ipynb  | 794 +++++++++++-------
 genindex.html                                 |   4 +-
 notebooks/04/dinner-seat-allocation.html      |   4 +-
 notebooks/04/gasoline-distribution.html       | 474 +++++------
 searchindex.js                                |   2 +-
 15 files changed, 937 insertions(+), 532 deletions(-)
 delete mode 100644 _images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png
 delete mode 100644 _images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png
 create mode 100644 _images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png
 create mode 100644 _images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg
 rename _images/{92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png => 50ec4a19840420dc3d3aecb825aa3214cbfa7081877adee3b8ad89bf91985bca.png} (98%)
 rename _images/{df69d323662a3f300a3b2b6a1bad82dc8b71938ab78f89ec600d3d225109f8de.png => 98ab93bd8361c7d84f3180808803d4b21d0cca066c8db29f83ae3012feadff2c.png} (97%)
 create mode 100644 _images/af2d6ffb05a0789e8f8c5abe8f69f66c2c62f3b23009b4e8b66c8d16b7313ae3.png
 rename _images/{b7cf18e448ef42aa288998b9fc6fb8513e528772d8cb4c281456ce4a9d944af1.png => f859de7d78ebccfd6dd6b4f485fae86ce571d705bbaf85dfdeb4f2c3e47c85eb.png} (98%)
 delete mode 100644 _images/seating_flow_model.png
 delete mode 100644 _images/seating_model_basic.png

diff --git a/_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png b/_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png
deleted file mode 100644
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+<!-- Pages: 1 -->
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+<text text-anchor="middle" x="415.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 50000</text>
+<text text-anchor="middle" x="415.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 50000.0</text>
+<text text-anchor="middle" x="415.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0875</text>
+</g>
+<!-- Terminal A&#45;&gt;Cara -->
+<g id="edge3" class="edge">
+<title>Terminal A&#45;&gt;Cara</title>
+<path fill="none" stroke="black" d="M261.54,-122.86C283.58,-115.58 309.44,-105.65 331.25,-93.5 347.6,-84.39 364.11,-72.21 378.11,-60.8"/>
+<polygon fill="black" stroke="black" points="380.33,-63.51 385.77,-54.41 375.84,-58.13 380.33,-63.51"/>
+<text text-anchor="middle" x="401.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.3</text>
+<text text-anchor="middle" x="401.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 12000.0</text>
+</g>
+<!-- Helen -->
+<g id="node11" class="node">
+<title>Helen</title>
+<polygon fill="gold" stroke="black" points="314.5,-53 222,-53 222,0 314.5,0 314.5,-53"/>
+<text text-anchor="middle" x="268.25" y="-39.5" font-family="Times,serif" font-size="10.00">Helen</text>
+<text text-anchor="middle" x="268.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 18000</text>
+<text text-anchor="middle" x="268.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 18000.0</text>
+<text text-anchor="middle" x="268.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0795</text>
+</g>
+<!-- Terminal A&#45;&gt;Helen -->
+<g id="edge4" class="edge">
+<title>Terminal A&#45;&gt;Helen</title>
+<path fill="none" stroke="black" d="M229.87,-111.21C233.49,-105.49 237.14,-99.37 240.25,-93.5 245.2,-84.15 249.95,-73.73 254.08,-63.98"/>
+<polygon fill="black" stroke="black" points="257.31,-65.34 257.89,-54.76 250.84,-62.66 257.31,-65.34"/>
+<text text-anchor="middle" x="289.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 7.5</text>
+<text text-anchor="middle" x="289.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 18000.0</text>
+</g>
+<!-- Terminal B -->
+<g id="node2" class="node">
+<title>Terminal B</title>
+<polygon fill="lightblue" stroke="black" points="963.5,-164.5 871,-164.5 871,-111.5 963.5,-111.5 963.5,-164.5"/>
+<text text-anchor="middle" x="917.25" y="-151" font-family="Times,serif" font-size="10.00">Terminal B</text>
+<text text-anchor="middle" x="917.25" y="-139.75" font-family="Times,serif" font-size="10.00">supply = 80000</text>
+<text text-anchor="middle" x="917.25" y="-128.5" font-family="Times,serif" font-size="10.00">shipped = 80000.0</text>
+<text text-anchor="middle" x="917.25" y="-117.25" font-family="Times,serif" font-size="10.00">sens &#160;= &#45;0.0075</text>
+</g>
+<!-- Dan -->
+<g id="node7" class="node">
+<title>Dan</title>
+<polygon fill="gold" stroke="black" points="1013.5,-53 921,-53 921,0 1013.5,0 1013.5,-53"/>
+<text text-anchor="middle" x="967.25" y="-39.5" font-family="Times,serif" font-size="10.00">Dan</text>
+<text text-anchor="middle" x="967.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 20000</text>
+<text text-anchor="middle" x="967.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 20000.0</text>
+<text text-anchor="middle" x="967.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0875</text>
+</g>
+<!-- Terminal B&#45;&gt;Dan -->
+<g id="edge5" class="edge">
+<title>Terminal B&#45;&gt;Dan</title>
+<path fill="none" stroke="black" d="M935.23,-111.39C938.83,-105.66 942.38,-99.49 945.25,-93.5 949.64,-84.34 953.52,-74.08 956.75,-64.42"/>
+<polygon fill="black" stroke="black" points="960.08,-65.5 959.78,-54.91 953.41,-63.38 960.08,-65.5"/>
+<text text-anchor="middle" x="991.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.0</text>
+<text text-anchor="middle" x="991.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 20000.0</text>
+</g>
+<!-- Grace -->
+<g id="node10" class="node">
+<title>Grace</title>
+<polygon fill="gold" stroke="black" points="863.5,-53 771,-53 771,0 863.5,0 863.5,-53"/>
+<text text-anchor="middle" x="817.25" y="-39.5" font-family="Times,serif" font-size="10.00">Grace</text>
+<text text-anchor="middle" x="817.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 80000</text>
+<text text-anchor="middle" x="817.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 80000.0</text>
+<text text-anchor="middle" x="817.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0875</text>
+</g>
+<!-- Terminal B&#45;&gt;Grace -->
+<g id="edge6" class="edge">
+<title>Terminal B&#45;&gt;Grace</title>
+<path fill="none" stroke="black" d="M883.34,-111.07C876.89,-105.55 870.41,-99.55 864.75,-93.5 855.97,-84.1 847.42,-73.03 840.06,-62.67"/>
+<polygon fill="black" stroke="black" points="843.08,-60.9 834.51,-54.67 837.33,-64.88 843.08,-60.9"/>
+<text text-anchor="middle" x="902.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.0</text>
+<text text-anchor="middle" x="902.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 60000.0</text>
+</g>
+<!-- Current Supplier -->
+<g id="node3" class="node">
+<title>Current Supplier</title>
+<polygon fill="lightblue" stroke="black" points="663.12,-164.5 565.38,-164.5 565.38,-111.5 663.12,-111.5 663.12,-164.5"/>
+<text text-anchor="middle" x="614.25" y="-151" font-family="Times,serif" font-size="10.00">Current Supplier</text>
+<text text-anchor="middle" x="614.25" y="-139.75" font-family="Times,serif" font-size="10.00">supply = 500000</text>
+<text text-anchor="middle" x="614.25" y="-128.5" font-family="Times,serif" font-size="10.00">shipped = 133000.0</text>
+<text text-anchor="middle" x="614.25" y="-117.25" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0</text>
+</g>
+<!-- Current Supplier&#45;&gt;Cara -->
+<g id="edge7" class="edge">
+<title>Current Supplier&#45;&gt;Cara</title>
+<path fill="none" stroke="black" d="M565,-126.65C538.98,-119.79 507.25,-109.11 481.75,-93.5 467.8,-84.96 454.54,-72.94 443.59,-61.49"/>
+<polygon fill="black" stroke="black" points="446.43,-59.41 437.07,-54.43 441.28,-64.15 446.43,-59.41"/>
+<text text-anchor="middle" x="520.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.75</text>
+<text text-anchor="middle" x="520.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 38000.0</text>
+</g>
+<!-- Emma -->
+<g id="node8" class="node">
+<title>Emma</title>
+<polygon fill="gold" stroke="black" points="716.5,-53 624,-53 624,0 716.5,0 716.5,-53"/>
+<text text-anchor="middle" x="670.25" y="-39.5" font-family="Times,serif" font-size="10.00">Emma</text>
+<text text-anchor="middle" x="670.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 30000</text>
+<text text-anchor="middle" x="670.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 30000.0</text>
+<text text-anchor="middle" x="670.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0875</text>
+</g>
+<!-- Current Supplier&#45;&gt;Emma -->
+<g id="edge8" class="edge">
+<title>Current Supplier&#45;&gt;Emma</title>
+<path fill="none" stroke="black" d="M634.4,-111.01C638.28,-105.41 642.1,-99.39 645.25,-93.5 650.2,-84.23 654.64,-73.77 658.35,-63.95"/>
+<polygon fill="black" stroke="black" points="661.6,-65.26 661.71,-54.67 655.02,-62.88 661.6,-65.26"/>
+<text text-anchor="middle" x="693.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.75</text>
+<text text-anchor="middle" x="693.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 30000.0</text>
+</g>
+<!-- Fujita -->
+<g id="node9" class="node">
+<title>Fujita</title>
+<polygon fill="gold" stroke="black" points="605.5,-53 513,-53 513,0 605.5,0 605.5,-53"/>
+<text text-anchor="middle" x="559.25" y="-39.5" font-family="Times,serif" font-size="10.00">Fujita</text>
+<text text-anchor="middle" x="559.25" y="-28.25" font-family="Times,serif" font-size="10.00">demand = 45000</text>
+<text text-anchor="middle" x="559.25" y="-17" font-family="Times,serif" font-size="10.00">shipped = 45000.0</text>
+<text text-anchor="middle" x="559.25" y="-5.75" font-family="Times,serif" font-size="10.00">sens &#160;= 0.0875</text>
+</g>
+<!-- Current Supplier&#45;&gt;Fujita -->
+<g id="edge9" class="edge">
+<title>Current Supplier&#45;&gt;Fujita</title>
+<path fill="none" stroke="black" d="M581.72,-111.28C576.67,-105.91 572.05,-99.91 568.75,-93.5 564.22,-84.7 561.65,-74.41 560.25,-64.61"/>
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+<text text-anchor="middle" x="607.5" y="-84" font-family="Times,serif" font-size="10.00">rate = 8.75</text>
+<text text-anchor="middle" x="607.5" y="-72.75" font-family="Times,serif" font-size="10.00">shipped = 45000.0</text>
+</g>
+<!-- Current Supplier&#45;&gt;Grace -->
+<g id="edge10" class="edge">
+<title>Current Supplier&#45;&gt;Grace</title>
+<path fill="none" stroke="black" d="M663.56,-123.38C686.17,-116.12 712.86,-106.06 735.25,-93.5 740.13,-90.76 759.14,-75.51 777.63,-60.36"/>
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+</g>
+</g>
+</svg>
\ No newline at end of file
diff --git a/_images/92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png b/_images/50ec4a19840420dc3d3aecb825aa3214cbfa7081877adee3b8ad89bf91985bca.png
similarity index 98%
rename from _images/92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png
rename to _images/50ec4a19840420dc3d3aecb825aa3214cbfa7081877adee3b8ad89bf91985bca.png
index 313bb911523619263f2d047a4e1b07908c790b23..0012164e876b81cc3d57d2cf1415050e774bdb65 100644
GIT binary patch
delta 43
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delta 43
ycmZ3UwLEKrn}WHXp^idENl8JmmA-y%Vo5<xeo0Pdl3spMy8dl0j`odd2TcH4q7Wkh

diff --git a/_images/df69d323662a3f300a3b2b6a1bad82dc8b71938ab78f89ec600d3d225109f8de.png b/_images/98ab93bd8361c7d84f3180808803d4b21d0cca066c8db29f83ae3012feadff2c.png
similarity index 97%
rename from _images/df69d323662a3f300a3b2b6a1bad82dc8b71938ab78f89ec600d3d225109f8de.png
rename to _images/98ab93bd8361c7d84f3180808803d4b21d0cca066c8db29f83ae3012feadff2c.png
index 641782b91f258cf1f5961425f827fe6283e640c8..654d79971b2bc77e73515246b882226e16b620bc 100644
GIT binary patch
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delta 43
ycmX?*b|7tnn}WHXp^idENl8JmmA-y%Vo5<xeo0Pdl3spMy8dl0j`oddw~PQ`z7S*p

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diff --git a/_sources/notebooks/04/gasoline-distribution.ipynb b/_sources/notebooks/04/gasoline-distribution.ipynb
index 2118332d..35434eb6 100644
--- a/_sources/notebooks/04/gasoline-distribution.ipynb
+++ b/_sources/notebooks/04/gasoline-distribution.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "```{index} single: application; transportation\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
+    "```{index} single: solver; highs\n",
     "```\n",
     "```{index} pandas dataframe\n",
     "```\n",
@@ -18,19 +18,8 @@
     "```\n",
     "\n",
     "# Gasoline distribution\n",
-    "\n",
-    "This notebook presents a transportation model to optimally allocate the delivery of a commodity from multiple sources to multiple destinations. The model invites a discussion of the pitfalls in optimizing a global objective for customers who may have an uneven share of the resulting benefits, then through model refinement arrives at a group cost-sharing plan to delivery costs.\n",
-    "\n",
-    "Didactically, notebook presents techniques for Pyomo modeling and reporting including: \n",
-    "\n",
-    "* `pyo.Expression` decorator\n",
-    "* Accessing the duals (i.e., shadow prices)\n",
-    "* Methods for reporting the solution and duals.\n",
-    "    * Pyomo `.display()` method for Pyomo objects\n",
-    "    * Manually formatted reports\n",
-    "    * Pandas \n",
-    "    * Graphviz for display of results as a directed graph.\n",
-    "   "
+    "   \n",
+    "This notebook presents a transportation model to optimally allocate the delivery of a commodity from multiple sources to multiple destinations. The model invites a discussion of the pitfalls in optimizing a global objective for customers who may have an uneven share of the resulting benefits, then through model refinement arrives at a group cost-sharing plan to delivery costs."
    ]
   },
   {
@@ -60,19 +49,32 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "\n",
+    " \n",
     "if 'google.colab' in sys.modules:\n",
     "    !pip install pyomo >/dev/null 2>/dev/null\n",
     "    !pip install highspy >/dev/null 2>/dev/null\n",
+    " \n",
+    "solver = 'appsi_highs'\n",
+    " \n",
+    "import pyomo.environ as pyo\n",
+    "SOLVER = pyo.SolverFactory(solver)\n",
+    " \n",
+    "assert SOLVER.available(), f\"Solver {solver} is not available.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Didactically, this notebook presents techniques for Pyomo modeling and reporting including: \n",
     "\n",
-    "    from pyomo.environ import SolverFactory\n",
-    "    SOLVER = SolverFactory('appsi_highs')\n",
-    "    \n",
-    "else:\n",
-    "    from pyomo.environ import SolverFactory\n",
-    "    SOLVER = SolverFactory('cbc')\n",
-    "\n",
-    "assert SOLVER.available(), f\"Solver {SOLVER} is not available.\""
+    "* `pyo.Expression` decorator\n",
+    "* Accessing the duals (i.e., shadow prices)\n",
+    "* Methods for reporting the solution and duals.\n",
+    "    * Pyomo `.display()` method for Pyomo objects\n",
+    "    * Manually formatted reports\n",
+    "    * Pandas \n",
+    "    * Graphviz for display of results as a directed graph."
    ]
   },
   {
@@ -84,25 +86,30 @@
    "source": [
     "## Problem: Distributing gasoline to franchise operators\n",
     "\n",
-    "YaYa Gas-n-Grub is franchiser and operator for a network of regional convenience stores selling gasoline and convenience items in the United States. Each store is individually owned by a YaYa Gas-n-Grub franchisee who pays a fee to the franchiser for services.\n",
-    "Gasoline is delivered by truck from regional distribution terminals. Each delivery truck carries 8,000 gallons delivered at a fixed charge of 700€ per delivery, or 0.0875€  per gallon. The franchise owners are eager to reduce delivery costs to boost profits.\n",
+    "YaYa Gas-n-Grub is the franchisor and operator of a network of regional convenience stores that sell gasoline and convenience items in the United States. Each store is individually owned by a YaYa Gas-n-Grub franchisee who pays fees to the franchisor for services. Gasoline is delivered by truck from regional distribution terminals by the current supplier. Each truck delivers 8,000 gallons at a fixed charge of \\$700 per delivery or \\$0.0875 per gallon. Franchise owners are eager to reduce delivery costs to boost profits.\n",
+    "\n",
     "YaYa Gas-n-Grub decides to accept proposals from other distribution terminals, \"A\" and \"B\", to supply the franchise operators. Rather than a fixed fee per delivery, they proposed pricing based on location. But they already have existing customers, \"A\" and \"B\" can only provide a limited amount of gasoline to new customers totaling 100,000 and 80,000 gallons respectively. The only difference between the new suppliers and the current supplier is the delivery charge.\n",
     "\n",
     "The following chart shows the pricing of gasoline delivery in cents/gallon.\n",
     "\n",
-    "| Franchisee <br> &nbsp; | Demand <br> &nbsp; |  Terminal A <br> 100,000| Terminal B <br> 80,000 | Current Supplier <br> 500,000 |\n",
-    "| :-------- | ------------: | :---------: | :-------: | :--------: |\n",
-    "| Alice | 30,000 | 8.3 | 10.2 | 8.75 |\n",
-    "| Badri  | 40,000 | 8.1 | 12.0 | 8.75 |\n",
-    "| Cara  | 50,000 | 8.3 | - | 8.75 |\n",
-    "| Dan   | 80,000 | 9.3 | 8.0 |  8.75 |\n",
-    "| Emma  | 30,000 | 10.1 | 10.0 | 8.75 |\n",
-    "| Fujita | 45,000 | 9.8 | 10.0 | 8.75 |\n",
-    "| Grace | 80,000 | -  | 8.0 | 8.75 |\n",
-    "| Helen | 18,000 | 7.5 | 10.0 | 8.75 |\n",
+    "<div align=\"center\">\n",
+    "\n",
+    "| Franchisee <br> &nbsp; | Demand <br> &nbsp; | Current Supplier <br> 500,000 |  Terminal A <br> 100,000| Terminal B <br> 80,000 |\n",
+    "| :-------- | ------------: | :--------: | :---------: | :-------: |\n",
+    "| Alice | 30,000 | 8.75 | 8.3 | 10.2 |\n",
+    "| Badri  | 40,000 | 8.75 | 8.1 | 12.0 |\n",
+    "| Cara  | 50,000 | 8.75 | 8.3 | - |\n",
+    "| Dan   | 80,000 | 8.75 | 9.3 | 8.0 |\n",
+    "| Emma  | 30,000 | 8.75 | 10.1 | 10.0 |\n",
+    "| Fujita | 45,000 | 8.75 | 9.8 | 10.0 |\n",
+    "| Grace | 80,000 | 8.75 | -  | 8.0 |\n",
+    "| Helen | 18,000 | 8.75 | 7.5 | 10.0 |\n",
     "| **TOTAL**| **313,000**| |  | | |\n",
     "\n",
-    "The operator of YaYa Gas-n-Grub has the challenge of allocating the gasoline delivery to minimize the cost to the franchise owners. The following model will present a global objective to minimize the total cost of delivery to all franchise owners. "
+    "\n",
+    "</div>\n",
+    "\n",
+    "The operator of YaYa Gas-n-Grub wants to allocate gasoline delivery in such a way that the costs to franchise owners are minimized."
    ]
   },
   {
@@ -111,14 +118,20 @@
    "source": [
     "## Model 1: Minimize total delivery cost\n",
     "\n",
-    "The decision variables for this example are labeled $x_{d, s}$ where subscript $d \\in 1, \\dots, n_d$ refers to the destination of the delivery and subscript $s \\in 1, \\dots, n_s$ to the source. The value of $x_{d,s}$ is the volume of gasoline shipped to destination $d$ from source $s$.\n",
-    "Given the cost rate $r_{d, s}$ for shipping one unit of goods from $d$ to $s$, the objective is to minimize the total cost of transporting gasoline from the sources to the destinations subject to meeting the demand requirements, $D_d$, at all destinations, and satisfying the supply constraints, $S_s$, at all sources. The full mathematical formulation is:\n",
+    "The first optimization model aims to minimize the total cost of delivery to all franchise owners. \n",
+    "\n",
+    "We introduce the decision variables $x_{d, s} \\geq 0$, where subscript $d \\in 1, \\dots, n_d$ refers to the destination of the delivery and subscript $s \\in 1, \\dots, n_s$ to the source. The value of $x_{d,s}$ is the volume of gasoline shipped to destination $d$ from source $s$.\n",
+    "\n",
+    "Given the cost rate $r_{d, s}$ for delivering one unit of gasoline from $d$ to $s$, the objective is to minimize the total cost of transporting gasoline from the sources to the destinations subject to meeting the demand requirements, $D_d$, at all destinations, and satisfying the supply constraints, $S_s$, at all sources. \n",
+    "\n",
+    "In mathematical terms, we can write the full problem as\n",
     "\n",
     "$$\n",
     "\\begin{align*}\n",
     "    \\min \\quad & \\sum_{d=1}^{n_d} \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\\\\n",
-    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d\\in 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
-    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s\\in 1, \\dots, n_s & \\quad \\text{(supply constraints)}\n",
+    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d = 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
+    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s = 1, \\dots, n_s & \\quad \\text{(supply constraints)}\\\\\n",
+    "    & x_{d, s} \\geq 0 & \\forall \\, d = 1, \\dots, n_d, \\, s = 1, \\dots, n_s.\n",
     "\\end{align*}\n",
     "$$\n"
    ]
@@ -134,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -309,7 +322,7 @@
     {
      "data": {
       "text/html": [
-       "<br><b>Transportation Rates (€ cents per Gallon)</b>"
+       "<br><b>Transportation Rates ($ cents per Gallon)</b>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -464,7 +477,7 @@
     "display(HTML(\"<br><b>Gasoline Demand (Gallons)</b>\"))\n",
     "display(demand.to_frame())\n",
     "\n",
-    "display(HTML(\"<br><b>Transportation Rates (€ cents per Gallon)</b>\"))\n",
+    "display(HTML(\"<br><b>Transportation Rates ($ cents per Gallon)</b>\"))\n",
     "display(rates)"
    ]
   },
@@ -477,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -502,8 +515,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Old Delivery Costs = 27387.50€\n",
-      "New Delivery Costs = 26113.50€\n",
+      "Old delivery costs = $27387.50\n",
+      "New delivery costs = $26113.50\n",
       "\n"
      ]
     },
@@ -541,91 +554,91 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alice</th>\n",
-       "      <td>30000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>2625.0</td>\n",
        "      <td>2490.0</td>\n",
        "      <td>135.0</td>\n",
-       "      <td>0.0830</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.30</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Badri</th>\n",
-       "      <td>40000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>3500.0</td>\n",
        "      <td>3240.0</td>\n",
        "      <td>260.0</td>\n",
-       "      <td>0.0810</td>\n",
-       "      <td>0.0855</td>\n",
+       "      <td>8.10</td>\n",
+       "      <td>8.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cara</th>\n",
-       "      <td>12000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>38000.0</td>\n",
+       "      <td>12000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38000</td>\n",
        "      <td>4375.0</td>\n",
        "      <td>4321.0</td>\n",
        "      <td>54.0</td>\n",
-       "      <td>0.0864</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.64</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dan</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20000.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>0</td>\n",
        "      <td>1750.0</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>150.0</td>\n",
-       "      <td>0.0800</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Emma</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>30000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30000</td>\n",
        "      <td>2625.0</td>\n",
        "      <td>2625.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fujita</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>45000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45000</td>\n",
        "      <td>3937.5</td>\n",
        "      <td>3937.5</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grace</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60000.0</td>\n",
-       "      <td>20000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>20000</td>\n",
        "      <td>7000.0</td>\n",
        "      <td>6550.0</td>\n",
        "      <td>450.0</td>\n",
-       "      <td>0.0819</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.19</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Helen</th>\n",
-       "      <td>18000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>18000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1575.0</td>\n",
        "      <td>1350.0</td>\n",
        "      <td>225.0</td>\n",
-       "      <td>0.0750</td>\n",
-       "      <td>0.0795</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>7.95</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -633,24 +646,24 @@
       ],
       "text/plain": [
        "        Terminal A  Terminal B  Current Supplier  current costs  \\\n",
-       "Alice      30000.0         0.0               0.0         2625.0   \n",
-       "Badri      40000.0         0.0               0.0         3500.0   \n",
-       "Cara       12000.0         0.0           38000.0         4375.0   \n",
-       "Dan            0.0     20000.0               0.0         1750.0   \n",
-       "Emma           0.0         0.0           30000.0         2625.0   \n",
-       "Fujita         0.0         0.0           45000.0         3937.5   \n",
-       "Grace          0.0     60000.0           20000.0         7000.0   \n",
-       "Helen      18000.0         0.0               0.0         1575.0   \n",
+       "Alice        30000           0                 0         2625.0   \n",
+       "Badri        40000           0                 0         3500.0   \n",
+       "Cara         12000           0             38000         4375.0   \n",
+       "Dan              0       20000                 0         1750.0   \n",
+       "Emma             0           0             30000         2625.0   \n",
+       "Fujita           0           0             45000         3937.5   \n",
+       "Grace            0       60000             20000         7000.0   \n",
+       "Helen        18000           0                 0         1575.0   \n",
        "\n",
        "        contract costs  savings  contract rate  marginal cost  \n",
-       "Alice           2490.0    135.0         0.0830         0.0875  \n",
-       "Badri           3240.0    260.0         0.0810         0.0855  \n",
-       "Cara            4321.0     54.0         0.0864         0.0875  \n",
-       "Dan             1600.0    150.0         0.0800         0.0875  \n",
-       "Emma            2625.0      0.0         0.0875         0.0875  \n",
-       "Fujita          3937.5      0.0         0.0875         0.0875  \n",
-       "Grace           6550.0    450.0         0.0819         0.0875  \n",
-       "Helen           1350.0    225.0         0.0750         0.0795  "
+       "Alice           2490.0    135.0           8.30           8.75  \n",
+       "Badri           3240.0    260.0           8.10           8.55  \n",
+       "Cara            4321.0     54.0           8.64           8.75  \n",
+       "Dan             1600.0    150.0           8.00           8.75  \n",
+       "Emma            2625.0      0.0           8.75           8.75  \n",
+       "Fujita          3937.5      0.0           8.75           8.75  \n",
+       "Grace           6550.0    450.0           8.19           8.75  \n",
+       "Helen           1350.0    225.0           7.50           7.95  "
       ]
      },
      "metadata": {},
@@ -658,7 +671,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -672,7 +685,7 @@
     "\n",
     "\n",
     "def transport(supply, demand, rates):\n",
-    "    m = pyo.ConcreteModel()\n",
+    "    m = pyo.ConcreteModel(\"Gasoline distribution\")\n",
     "\n",
     "    m.SOURCES = pyo.Set(initialize=rates.columns)\n",
     "    m.DESTINATIONS = pyo.Set(initialize=rates.index)\n",
@@ -718,7 +731,7 @@
     "m = transport(supply, demand, rates / 100)\n",
     "\n",
     "results = pd.DataFrame(\n",
-    "    {dst: {src: m.x[dst, src]() for src in m.SOURCES} for dst in m.DESTINATIONS}\n",
+    "    {dst: {src: round(m.x[dst, src]()) for src in m.SOURCES} for dst in m.DESTINATIONS}\n",
     ").T\n",
     "results[\"current costs\"] = 700 * demand / 8000\n",
     "results[\"contract costs\"] = pd.Series(\n",
@@ -727,13 +740,13 @@
     "results[\"savings\"] = results[\"current costs\"].round(1) - results[\n",
     "    \"contract costs\"\n",
     "].round(1)\n",
-    "results[\"contract rate\"] = round(results[\"contract costs\"] / demand, 4)\n",
-    "results[\"marginal cost\"] = pd.Series(\n",
+    "results[\"contract rate\"] = 100 * round(results[\"contract costs\"] / demand, 4)\n",
+    "results[\"marginal cost\"] = 100 * pd.Series(\n",
     "    {dst: m.dual[m.demand_constraint[dst]] for dst in m.DESTINATIONS}\n",
     ")\n",
     "\n",
-    "print(f\"Old Delivery Costs = {sum(demand)*700/8000:.2f}€\")\n",
-    "print(f\"New Delivery Costs = {m.total_cost():.2f}€\\n\")\n",
+    "print(f\"Old delivery costs = ${sum(demand)*700/8000:.2f}\")\n",
+    "print(f\"New delivery costs = ${m.total_cost():.2f}\\n\")\n",
     "display(results)\n",
     "\n",
     "results.plot(y=\"savings\", kind=\"bar\")\n",
@@ -746,14 +759,17 @@
    "source": [
     "## Model 2: Minimize cost rate for franchise owners\n",
     "\n",
-    "Minimizing total costs provides no guarantee that individual franchise owners will benefit equally, or in fact benefit at all, from minimizing total costs. In this example neither Emma or Fujita would save any money on delivery costs, and the majority of savings goes to just two of the franchisees.  Without a better distribution of the benefits there may be little enthusiasm among the franchisees to adopt change. This observation motivates an attempt at a second model. In this case the objective is minimize a common rate for the cost of gasoline distribution subject to meeting the demand and supply constraints, $S_s$, at all sources. The mathematical formulation of this different problem is as follows:\n",
+    "Minimizing total costs provides no guarantee that individual franchise owners will benefit equally, or in fact benefit at all, from minimizing total costs. In this example neither Emma or Fujita would save any money on delivery costs, and the majority of savings goes to just one of the franchisees. Without a better distribution of the benefits there may be little enthusiasm among the franchisees to adopt change. This observation motivates an attempt at a second model. In this case the objective is minimizing a common rate for the cost of gasoline distribution subject to meeting the demand and supply constraints, $S_s$, at all sources. \n",
+    "\n",
+    "The mathematical formulation of this different problem is as follows:\n",
     "\n",
     "$$\n",
     "\\begin{align*}\n",
     "    \\min \\quad & \\rho \\\\\n",
-    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d\\in 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
-    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s\\in 1, \\dots, n_s & \\quad \\text{(supply constraints)}\\\\\n",
-    "    & \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\leq \\rho D_d & \\forall d\\in 1, \\dots, n_d & \\quad \\text{(common cost rate)}\\\\\n",
+    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d = 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
+    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s =  1, \\dots, n_s & \\quad \\text{(supply constraints)}\\\\\n",
+    "    & \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\leq \\rho D_d & \\forall d = 1, \\dots, n_d & \\quad \\text{(common cost rate)}\\\\\n",
+    "    & x_{d, s} \\geq 0 & \\forall \\, d = 1, \\dots, n_d, \\, s = 1, \\dots, n_s.\n",
     "\\end{align*}\n",
     "$$\n",
     "\n",
@@ -762,15 +778,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Old Delivery Costs = $ 27387.5\n",
-      "New Delivery Costs = $ 27387.49999\n"
+      "Old delivery costs = $27387.5\n",
+      "New delivery costs = $27387.5\n"
      ]
     },
     {
@@ -813,8 +829,8 @@
        "      <td>2625.0</td>\n",
        "      <td>2625.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Badri</th>\n",
@@ -824,19 +840,19 @@
        "      <td>3500.0</td>\n",
        "      <td>3500.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cara</th>\n",
+       "      <td>49754.6</td>\n",
+       "      <td>245.4</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>50000.0</td>\n",
        "      <td>4375.0</td>\n",
        "      <td>4375.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000002</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dan</th>\n",
@@ -846,8 +862,8 @@
        "      <td>1750.0</td>\n",
        "      <td>1750.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Emma</th>\n",
@@ -857,8 +873,8 @@
        "      <td>2625.0</td>\n",
        "      <td>2625.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fujita</th>\n",
@@ -868,30 +884,30 @@
        "      <td>3937.5</td>\n",
        "      <td>3937.5</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grace</th>\n",
-       "      <td>652.2</td>\n",
-       "      <td>79347.8</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>80000.0</td>\n",
        "      <td>7000.0</td>\n",
        "      <td>7000.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Helen</th>\n",
-       "      <td>-0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>18000.0</td>\n",
        "      <td>1575.0</td>\n",
        "      <td>1575.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0875</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -901,22 +917,22 @@
        "        Terminal A  Terminal B  Current Supplier  current costs  \\\n",
        "Alice          0.0         0.0           30000.0         2625.0   \n",
        "Badri          0.0         0.0           40000.0         3500.0   \n",
-       "Cara           0.0         0.0           50000.0         4375.0   \n",
+       "Cara       49754.6       245.4               0.0         4375.0   \n",
        "Dan            0.0         0.0           20000.0         1750.0   \n",
        "Emma           0.0         0.0           30000.0         2625.0   \n",
        "Fujita         0.0         0.0           45000.0         3937.5   \n",
-       "Grace        652.2     79347.8               0.0         7000.0   \n",
-       "Helen         -0.0         0.0           18000.0         1575.0   \n",
+       "Grace          0.0         0.0           80000.0         7000.0   \n",
+       "Helen          0.0         0.0           18000.0         1575.0   \n",
        "\n",
        "        contract costs  savings  contract rate  marginal cost  \n",
-       "Alice           2625.0      0.0         0.0875       0.000000  \n",
-       "Badri           3500.0      0.0         0.0875       0.000000  \n",
-       "Cara            4375.0      0.0         0.0875       0.000002  \n",
-       "Dan             1750.0      0.0         0.0875       0.000000  \n",
-       "Emma            2625.0      0.0         0.0875       0.000000  \n",
-       "Fujita          3937.5      0.0         0.0875       0.000000  \n",
-       "Grace           7000.0      0.0         0.0875       0.000000  \n",
-       "Helen           1575.0      0.0         0.0875       0.000000  "
+       "Alice           2625.0      0.0           8.75            0.0  \n",
+       "Badri           3500.0      0.0           8.75            0.0  \n",
+       "Cara            4375.0      0.0           8.75            0.0  \n",
+       "Dan             1750.0      0.0           8.75            0.0  \n",
+       "Emma            2625.0      0.0           8.75            0.0  \n",
+       "Fujita          3937.5      0.0           8.75            0.0  \n",
+       "Grace           7000.0      0.0           8.75            0.0  \n",
+       "Helen           1575.0      0.0           8.75            0.0  "
       ]
      },
      "metadata": {},
@@ -924,7 +940,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -937,7 +953,7 @@
     "import pyomo.environ as pyo\n",
     "\n",
     "\n",
-    "def transport(supply, demand, rates):\n",
+    "def transport_v2(supply, demand, rates):\n",
     "    m = pyo.ConcreteModel()\n",
     "\n",
     "    m.SOURCES = pyo.Set(initialize=rates.columns)\n",
@@ -988,7 +1004,7 @@
     "    return m\n",
     "\n",
     "\n",
-    "m = transport(supply, demand, rates / 100)\n",
+    "m = transport_v2(supply, demand, rates / 100)\n",
     "\n",
     "results = round(\n",
     "    pd.DataFrame(\n",
@@ -1003,13 +1019,13 @@
     "results[\"savings\"] = results[\"current costs\"].round(1) - results[\n",
     "    \"contract costs\"\n",
     "].round(1)\n",
-    "results[\"contract rate\"] = round(results[\"contract costs\"] / demand, 4)\n",
-    "results[\"marginal cost\"] = pd.Series(\n",
-    "    {dst: m.dual[m.demand_constraint[dst]] for dst in m.DESTINATIONS}\n",
+    "results[\"contract rate\"] = 100 * round(results[\"contract costs\"] / demand, 4)\n",
+    "results[\"marginal cost\"] = 100 * pd.Series(\n",
+    "    {dst: round(m.dual[m.demand_constraint[dst]], 4) for dst in m.DESTINATIONS}\n",
     ")\n",
     "\n",
-    "print(f\"Old Delivery Costs = $ {sum(demand)*700/8000}\")\n",
-    "print(f\"New Delivery Costs = $ {m.total_cost()}\")\n",
+    "print(f\"Old delivery costs = ${sum(demand)*700/8000}\")\n",
+    "print(f\"New delivery costs = ${round(m.total_cost(),1)}\")\n",
     "display(results)\n",
     "\n",
     "results.plot(y=\"savings\", kind=\"bar\")\n",
@@ -1022,35 +1038,36 @@
    "source": [
     "## Model 3: Minimize total cost for a cost-sharing plan\n",
     "\n",
-    "The prior two models demonstrated some practical difficulties in realizing the benefits of a cost optimization plan. Model 1 will likely fail in a franchiser/franchisee arrangement because the realized savings would be fore the benefit of a few. \n",
+    "The prior two models demonstrated some practical difficulties in realizing the benefits of a cost optimization plan. Model 1 will likely fail in a franchiser/franchisee arrangement because the realized savings would be for the benefit of a few. \n",
     "\n",
-    "Model 2 was an attempt to remedy the problem by solving for an allocation of deliveries that would lower the cost rate that would be paid by each franchisee directly to the gasoline distributors. Perhaps surprisingly, the resulting solution offered no savings to any franchisee. Inspecting the data shows the source of the problem is that two franchisees, Emma and Fujita, simply have no lower cost alternative than the current supplier. Therefore finding a distribution plan with direct payments to the distributors that lowers everyone's cost is an impossible task.\n",
+    "Model 2 was an attempt to remedy the problem by solving for an allocation of deliveries that would lower the cost rate that would be paid by each franchisee directly to the gasoline distributors. Perhaps surprisingly, the resulting solution offered no savings to any franchisee. Inspecting the data shows the source of the problem is that two franchisees, Emma and Fujita, simply have no lower cost alternative than the current supplier. Therefore, finding a distribution plan with direct payments to the distributors that lowers everyone's cost is an impossible task.\n",
     "\n",
-    "The third model addresses this problem with a plan to share the cost savings among the franchisees. In this plan, the franchiser would collect delivery fees from the franchisees to pay the gasoline distributors. The optimization objective returns to the problem to minimizing total delivery costs, but then adds a constraint that defines a common cost rate to charge all franchisees. By offering a benefit to all parties, the franchiser offers incentive for group participation in contracting for gasoline distribution services.\n",
+    "We now consider a third model that addresses this problem with a plan to share the cost savings among the franchisees. In this plan, the franchiser would collect delivery fees from the franchisees to pay the gasoline distributors. The optimization objective returns to the problem to minimizing total delivery costs, but then adds a constraint that defines a common cost rate to charge all franchisees. By offering a benefit to all parties, the franchiser offers incentive for group participation in contracting for gasoline distribution services.\n",
     "\n",
     "In mathematical terms, the problem can be formulated as follows:\n",
     "\n",
     "$$\n",
     "\\begin{align*}\n",
     "    \\min \\quad & \\sum_{d=1}^{n_d} \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\\\\n",
-    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d\\in 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
-    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s\\in 1, \\dots, n_s & \\quad \\text{(supply constraints)}\\\\\n",
-    "    & \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} = \\rho D_d & \\forall d\\in 1, \\dots, n_d & \\quad \\text{(uniform cost sharing rate)}\\\\\n",
+    "    \\text{s.t.} \\quad &\\sum_{s=1}^{n_s} x_{d, s}  = D_d & \\forall \\, d= 1, \\dots, n_d & \\quad \\text{(demand constraints)}\\\\\n",
+    "    & \\sum_{d=1}^{n_d} x_{d, s}  \\leq S_s & \\forall \\, s = 1, \\dots, n_s & \\quad \\text{(supply constraints)}\\\\\n",
+    "    & \\sum_{s=1}^{n_s} r_{d, s} x_{d, s} = \\rho D_d & \\forall d= 1, \\dots, n_d & \\quad \\text{(uniform cost sharing rate)}\\\\\n",
+    "    & x_{d, s} \\geq 0 & \\forall \\, d = 1, \\dots, n_d, \\, s = 1, \\dots, n_s.\n",
     "\\end{align*}\n",
     "$$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Old Delivery Costs = 27387.50€\n",
-      "New Delivery Costs = 26113.50€\n",
+      "Old delivery costs = $27387.50\n",
+      "New delivery costs = $26113.50\n",
       "\n"
      ]
     },
@@ -1094,8 +1111,8 @@
        "      <td>2625.0</td>\n",
        "      <td>2502.9</td>\n",
        "      <td>122.1</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Badri</th>\n",
@@ -1105,8 +1122,8 @@
        "      <td>3500.0</td>\n",
        "      <td>3337.2</td>\n",
        "      <td>162.8</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0855</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cara</th>\n",
@@ -1116,8 +1133,8 @@
        "      <td>4375.0</td>\n",
        "      <td>4171.5</td>\n",
        "      <td>203.5</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dan</th>\n",
@@ -1127,8 +1144,8 @@
        "      <td>1750.0</td>\n",
        "      <td>1668.6</td>\n",
        "      <td>81.4</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Emma</th>\n",
@@ -1138,8 +1155,8 @@
        "      <td>2625.0</td>\n",
        "      <td>2502.9</td>\n",
        "      <td>122.1</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fujita</th>\n",
@@ -1149,8 +1166,8 @@
        "      <td>3937.5</td>\n",
        "      <td>3754.3</td>\n",
        "      <td>183.2</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grace</th>\n",
@@ -1160,8 +1177,8 @@
        "      <td>7000.0</td>\n",
        "      <td>6674.4</td>\n",
        "      <td>325.6</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0875</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>8.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Helen</th>\n",
@@ -1171,8 +1188,8 @@
        "      <td>1575.0</td>\n",
        "      <td>1501.7</td>\n",
        "      <td>73.3</td>\n",
-       "      <td>0.0834</td>\n",
-       "      <td>0.0795</td>\n",
+       "      <td>8.34</td>\n",
+       "      <td>7.95</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1190,14 +1207,14 @@
        "Helen      18000.0         0.0               0.0         1575.0   \n",
        "\n",
        "        contract costs  savings  contract rate  marginal cost  \n",
-       "Alice           2502.9    122.1         0.0834         0.0875  \n",
-       "Badri           3337.2    162.8         0.0834         0.0855  \n",
-       "Cara            4171.5    203.5         0.0834         0.0875  \n",
-       "Dan             1668.6     81.4         0.0834         0.0875  \n",
-       "Emma            2502.9    122.1         0.0834         0.0875  \n",
-       "Fujita          3754.3    183.2         0.0834         0.0875  \n",
-       "Grace           6674.4    325.6         0.0834         0.0875  \n",
-       "Helen           1501.7     73.3         0.0834         0.0795  "
+       "Alice           2502.9    122.1           8.34           8.75  \n",
+       "Badri           3337.2    162.8           8.34           8.55  \n",
+       "Cara            4171.5    203.5           8.34           8.75  \n",
+       "Dan             1668.6     81.4           8.34           8.75  \n",
+       "Emma            2502.9    122.1           8.34           8.75  \n",
+       "Fujita          3754.3    183.2           8.34           8.75  \n",
+       "Grace           6674.4    325.6           8.34           8.75  \n",
+       "Helen           1501.7     73.3           8.34           7.95  "
       ]
      },
      "metadata": {},
@@ -1205,7 +1222,7 @@
     },
     {
      "data": {
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",
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iIyMLUw4AAPBQhbrEM3ToUC1evFjffPONAgMDnXNGgoKCFBAQoNTUVC1evFhdunRRhQoVtHPnTo0cOVL333+/GjduLEnq1KmTIiMj1a9fP02bNk0Oh0Pjx4/X0KFD5e/vX/RnCAAASpxCjaDMmTNHGRkZateunSpXruzcli5dKkny8/PTDz/8oE6dOqlBgwZ66aWX1LNnT61YscL5Ht7e3lq5cqW8vb0VHR2tvn376umnn3ZZNwUAAJRuhRpBMQzjuu0RERFKSkq64fvUqFFD3333XWEODQAAShGexQMAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACzHpzCdp0yZomXLlmn//v0KCAjQPffcozfffFP169d39jl//rxeeuklLVmyRDk5OYqNjdXs2bMVFhbm7JOWlqYhQ4Zo3bp1Kl++vPr3768pU6bIx6dQ5QA3rebYb0079pGpXU07NgCUVIUaQUlKStLQoUO1ceNGrVmzRhcvXlSnTp2UnZ3t7DNy5EitWLFCX375pZKSknTs2DH16NHD2Z6bm6uuXbvqwoUL2rBhgz7++GMtXLhQEyZMKLqzAgAAJZrNMAzjVl988uRJhYaGKikpSffff78yMjJUqVIlLV68WI899pgkaf/+/WrYsKGSk5PVpk0brVq1Sg899JCOHTvmHFWZO3euxowZo5MnT8rPz++Gx83MzFRQUJAyMjJkt9tvtXyUIoygAJ6L7++SozC/v29rDkpGRoYkKSQkRJK0detWXbx4UTExMc4+DRo0UPXq1ZWcnCxJSk5OVlRUlMsln9jYWGVmZmrPnj3XPE5OTo4yMzNdNgAA4LluOaDk5eXpxRdfVNu2bXXXXXdJkhwOh/z8/BQcHOzSNywsTA6Hw9nnz+HkSvuVtmuZMmWKgoKCnFtERMStlg0AAEqAWw4oQ4cO1e7du7VkyZKirOeaxo0bp4yMDOd29OhRtx8TAACY55Zum4mPj9fKlSu1fv16VatWzbk/PDxcFy5c0JkzZ1xGUdLT0xUeHu7s88svv7i8X3p6urPtWvz9/eXv738rpQIAgBKoUCMohmEoPj5eX3/9tdauXatatWq5tLdo0UK+vr5KTEx07jtw4IDS0tIUHR0tSYqOjtauXbt04sQJZ581a9bIbrcrMjLyds4FAAB4iEKNoAwdOlSLFy/WN998o8DAQOeckaCgIAUEBCgoKEiDBg1SQkKCQkJCZLfbNWzYMEVHR6tNmzaSpE6dOikyMlL9+vXTtGnT5HA4NH78eA0dOpRREgAAIKmQAWXOnDmSpHbt2rnsX7BggQYMGCBJmj59ury8vNSzZ0+Xhdqu8Pb21sqVKzVkyBBFR0erXLly6t+/vyZNmnR7ZwIAADxGoQLKzSyZUqZMGc2aNUuzZs0qsE+NGjX03XffFebQAACgFOFZPAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIK9SweAIB11Rz7rWnHPjK1q2nHhmdiBAUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFgOAQUAAFiOj9kFoHjVHPutacc+MrWraccGAJQsjKAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLKXRAWb9+vbp166YqVarIZrNp+fLlLu0DBgyQzWZz2Tp37uzS5/Tp0+rTp4/sdruCg4M1aNAgZWVl3daJAAAAz1HogJKdna0mTZpo1qxZBfbp3Lmzjh8/7tw+//xzl/Y+ffpoz549WrNmjVauXKn169frueeeK3z1AADAIxX6WTxxcXGKi4u7bh9/f3+Fh4dfs23fvn1avXq1Nm/erJYtW0qS3nvvPXXp0kVvvfWWqlSpUtiSAACAh3HLHJQff/xRoaGhql+/voYMGaJTp04525KTkxUcHOwMJ5IUExMjLy8vbdq06Zrvl5OTo8zMTJcNAAB4riIPKJ07d9Ynn3yixMREvfnmm0pKSlJcXJxyc3MlSQ6HQ6GhoS6v8fHxUUhIiBwOxzXfc8qUKQoKCnJuERERRV02AACwkEJf4rmRXr16OT+OiopS48aNVadOHf3444/q2LHjLb3nuHHjlJCQ4Pw8MzOTkAIAgAdz+23GtWvXVsWKFXXo0CFJUnh4uE6cOOHS59KlSzp9+nSB81b8/f1lt9tdNgAA4LncHlD++c9/6tSpU6pcubIkKTo6WmfOnNHWrVudfdauXau8vDy1bt3a3eUAAIASoNCXeLKyspyjIZJ0+PBhpaSkKCQkRCEhIXr99dfVs2dPhYeHKzU1VaNHj1bdunUVGxsrSWrYsKE6d+6swYMHa+7cubp48aLi4+PVq1cv7uABAACSbmEEZcuWLWrWrJmaNWsmSUpISFCzZs00YcIEeXt7a+fOnerevbvuvPNODRo0SC1atND//M//yN/f3/keixYtUoMGDdSxY0d16dJF9957rz744IOiOysAAFCiFXoEpV27djIMo8D277///obvERISosWLFxf20AAAoJTgWTwAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByfMwuwCw1x35r2rGPTO1q2rGB0oDvb6DkYwQFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYDgEFAABYTqEDyvr169WtWzdVqVJFNptNy5cvd2k3DEMTJkxQ5cqVFRAQoJiYGP3jH/9w6XP69Gn16dNHdrtdwcHBGjRokLKysm7rRAAAgOcodEDJzs5WkyZNNGvWrGu2T5s2TTNnztTcuXO1adMmlStXTrGxsTp//ryzT58+fbRnzx6tWbNGK1eu1Pr16/Xcc8/d+lkAAACP4lPYF8TFxSkuLu6abYZh6N1339X48eP18MMPS5I++eQThYWFafny5erVq5f27dun1atXa/PmzWrZsqUk6b333lOXLl301ltvqUqVKrdxOgAAwBMU6RyUw4cPy+FwKCYmxrkvKChIrVu3VnJysiQpOTlZwcHBznAiSTExMfLy8tKmTZuu+b45OTnKzMx02QAAgOcq0oDicDgkSWFhYS77w8LCnG0Oh0OhoaEu7T4+PgoJCXH2udqUKVMUFBTk3CIiIoqybAAAYDEl4i6ecePGKSMjw7kdPXrU7JIAAIAbFWlACQ8PlySlp6e77E9PT3e2hYeH68SJEy7tly5d0unTp519rubv7y+73e6yAQAAz1WkAaVWrVoKDw9XYmKic19mZqY2bdqk6OhoSVJ0dLTOnDmjrVu3OvusXbtWeXl5at26dVGWAwAASqhC38WTlZWlQ4cOOT8/fPiwUlJSFBISourVq+vFF1/Uf/7nf6pevXqqVauWXn31VVWpUkWPPPKIJKlhw4bq3LmzBg8erLlz5+rixYuKj49Xr169uIMHAABIuoWAsmXLFrVv3975eUJCgiSpf//+WrhwoUaPHq3s7Gw999xzOnPmjO69916tXr1aZcqUcb5m0aJFio+PV8eOHeXl5aWePXtq5syZRXA6AADAExQ6oLRr106GYRTYbrPZNGnSJE2aNKnAPiEhIVq8eHFhDw0AAEqJEnEXDwAAKF0IKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIIKAAAwHIK/TRjACVHzbHfmnbsI1O7mnZsACUfIygAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByWKgNAIASyNMXYmQEBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWE6RB5TXXntNNpvNZWvQoIGz/fz58xo6dKgqVKig8uXLq2fPnkpPTy/qMgAAQAnmlhGURo0a6fjx487tp59+craNHDlSK1as0JdffqmkpCQdO3ZMPXr0cEcZAACghPJxy5v6+Cg8PDzf/oyMDM2bN0+LFy9Whw4dJEkLFixQw4YNtXHjRrVp08Yd5QAAgBLGLSMo//jHP1SlShXVrl1bffr0UVpamiRp69atunjxomJiYpx9GzRooOrVqys5ObnA98vJyVFmZqbLBgAAPFeRB5TWrVtr4cKFWr16tebMmaPDhw/rvvvu09mzZ+VwOOTn56fg4GCX14SFhcnhcBT4nlOmTFFQUJBzi4iIKOqyAQCAhRT5JZ64uDjnx40bN1br1q1Vo0YNffHFFwoICLil9xw3bpwSEhKcn2dmZhJSAADwYG6/zTg4OFh33nmnDh06pPDwcF24cEFnzpxx6ZOenn7NOStX+Pv7y263u2wAAMBzuT2gZGVlKTU1VZUrV1aLFi3k6+urxMREZ/uBAweUlpam6Ohod5cCAABKiCK/xPPyyy+rW7duqlGjho4dO6aJEyfK29tbTz31lIKCgjRo0CAlJCQoJCREdrtdw4YNU3R0NHfwAAAApyIPKP/85z/11FNP6dSpU6pUqZLuvfdebdy4UZUqVZIkTZ8+XV5eXurZs6dycnIUGxur2bNnF3UZAACgBCvygLJkyZLrtpcpU0azZs3SrFmzivrQAADAQ/AsHgAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDkEFAAAYDmmBpRZs2apZs2aKlOmjFq3bq1ffvnFzHIAAIBFmBZQli5dqoSEBE2cOFHbtm1TkyZNFBsbqxMnTphVEgAAsAjTAso777yjwYMHa+DAgYqMjNTcuXNVtmxZzZ8/36ySAACARfiYcdALFy5o69atGjdunHOfl5eXYmJilJycnK9/Tk6OcnJynJ9nZGRIkjIzM2+5hrycc7f82tt1O3XfLs67+HHexY/zLn6cd/Eried95XWGYdy4s2GCf/3rX4YkY8OGDS77R40aZbRq1Spf/4kTJxqS2NjY2NjY2DxgO3r06A2zgikjKIU1btw4JSQkOD/Py8vT6dOnVaFCBdlstmKtJTMzUxERETp69KjsdnuxHttMnDfnXRpw3px3aWDmeRuGobNnz6pKlSo37GtKQKlYsaK8vb2Vnp7usj89PV3h4eH5+vv7+8vf399lX3BwsDtLvCG73V6qvqCv4LxLF867dOG8SxezzjsoKOim+pkySdbPz08tWrRQYmKic19eXp4SExMVHR1tRkkAAMBCTLvEk5CQoP79+6tly5Zq1aqV3n33XWVnZ2vgwIFmlQQAACzCtIDy5JNP6uTJk5owYYIcDoeaNm2q1atXKywszKySboq/v78mTpyY75KTp+O8Oe/SgPPmvEuDknLeNsO4mXt9AAAAig/P4gEAAJZDQAEAAJZDQAEAAJZDQAEAAJZDQAEAAJZDQAEAAJZTIp7FY7bU1FQtWLBAqampmjFjhkJDQ7Vq1SpVr15djRo1Mrs8uEF2draSkpKUlpamCxcuuLQNHz7cpKoAFIUzZ87oq6++UmpqqkaNGqWQkBBt27ZNYWFhqlq1qtnl4X+xDsoNJCUlKS4uTm3bttX69eu1b98+1a5dW1OnTtWWLVv01VdfmV1ikZo5c6aee+45lSlTRjNnzrxuX0/9Rb19+3Z16dJF586dU3Z2tkJCQvTvf/9bZcuWVWhoqH799VezS3SL9PR0vfzyy0pMTNSJEyfyPQ49NzfXpMqKz/nz5/MFUk9+RsuWLVv0xRdfXDOIL1u2zKSq3Gvnzp2KiYlRUFCQjhw5ogMHDqh27doaP3680tLS9Mknn5hdoltkZ2dr6tSpzu/vvLw8l3Yr/lwjoNxAdHS0Hn/8cSUkJCgwMFA7duxQ7dq19csvv6hHjx765z//aXaJRapWrVrasmWLKlSooFq1ahXYz2azWfILuii0a9dOd955p+bOnaugoCDt2LFDvr6+6tu3r0aMGKEePXqYXaJbxMXFKS0tTfHx8apcuXK+J4U//PDDJlXmXufOndPo0aP1xRdf6NSpU/naPTWYLVmyRE8//bRiY2P197//XZ06ddLBgweVnp6uRx99VAsWLDC7RLeIiYlR8+bNNW3aNJef6Rs2bFDv3r115MgRs0t0i6eeekpJSUnq16/fNb+/R4wYYVJl12HgusqVK2f8+uuvhmEYRvny5Y3U1FTDMAzj8OHDhr+/v5mlwU2CgoKM/fv3Oz/eu3evYRiGsXHjRqN+/fpmluZW5cuXN7Zv3252GcXuhRdeMBo2bGh89dVXRkBAgDF//nzjjTfeMKpVq2Z89tlnZpfnNlFRUcb7779vGMb//WzLy8szBg8ebEyYMMHk6tzHbrcbhw4dMgzD9Wf6kSNHPPpnelBQkPHTTz+ZXUahMEn2BoKDg3X8+PF8+7dv3+7R1yovXryoOnXqaN++fWaXUux8fX3l5XX5WyM0NFRpaWmSLj8i/OjRo2aW5lYRERH5LuuUBitWrNDs2bPVs2dP+fj46L777tP48eM1efJkLVq0yOzy3CY1NVVdu3aVdPkJ89nZ2bLZbBo5cqQ++OADk6tzH39/f2VmZubbf/DgQVWqVMmEiorHHXfcoZCQELPLKBQCyg306tVLY8aMkcPhkM1mU15enn7++We9/PLLevrpp80uz218fX11/vx5s8swRbNmzbR582ZJ0gMPPKAJEyZo0aJFevHFF3XXXXeZXJ37vPvuuxo7dqzHDnEX5PTp06pdu7aky/NNTp8+LUm69957tX79ejNLc6s77rhDZ8+elSRVrVpVu3fvlnR5Aum5c+fMLM2tunfvrkmTJunixYuSLl+uTktL05gxY9SzZ0+Tq3OfN954QxMmTChZ/7dmD+FYXU5OjvHss88aPj4+hs1mM3x9fQ0vLy+jb9++xqVLl8wuz63+8pe/GP379zcuXrxodinFavPmzcbatWsNwzCM9PR0IzY21ggMDDSaN29upKSkmFyd+wQHBxt+fn6Gl5eXUb58eeOOO+5w2TxVVFSU8eOPPxqGYRgdO3Y0XnrpJcMwDGPGjBlG1apVzSzNrZ566inj7bffNgzDMCZNmmRUqlTJePbZZ40aNWoYjz76qMnVuc+ZM2eMmJgYIzg42PD29jYiIiIMX19f4/777zeysrLMLs9tmjZtagQGBhrly5c37rrrLqNZs2YumxUxSfYmHT16VLt27VJWVpaaNWumevXqmV2S2z366KNKTExU+fLlFRUVpXLlyrm0e+Isf8MwdPToUYWGhqpMmTJml1OsPv744+u29+/fv5gqKV7Tp0+Xt7e3hg8frh9++EHdunWTYRi6ePGi3nnnHWtOHiwCp0+f1vnz51WlShXl5eVp2rRp2rBhg+rVq6fx48frjjvuMLtEt/r555+1Y8cOZWVlqXnz5oqJiTG7JLd6/fXXr9s+ceLEYqrk5hFQUKCBAwdet90TZ/nn5eWpTJky2rNnT6kIocjvt99+09atW1W3bl01btzY7HKAUouAcgM9e/ZUq1atNGbMGJf906ZN0+bNm/Xll1+aVBncpVGjRpo3b57atGljdimmKW3rgZRG3t7eOn78uEJDQ132nzp1SqGhoR57e/Xw4cNVt27dfOs4vf/++zp06JDeffddcworBiVtgToCyg1UqlRJa9euVVRUlMv+Xbt2KSYmRunp6SZVBndZsWKFpk2bpjlz5nj0pNirZWdna8yYMaVuPRBJ2rx5s9atW3fNBazeeecdk6pyLy8vLzkcjnwB5dixY6pTp47++OMPkypzr6pVq+pvf/ubWrRo4bJ/27Zt6t69u8etbXVFSVygjqXubyArK0t+fn759vv6+l7zVrWSrlmzZvkW8CnItm3b3FyNOZ5++mmdO3dOTZo0kZ+fnwICAlzar9zl4WlGjx6tdevWac6cOerXr59mzZqlf/3rX/rrX/+qqVOnml2e20yePFnjx49X/fr1FRYW5vL1f7PfCyXJlRWibTabPvroI5UvX97Zlpubq/Xr16tBgwZmled2p06dUlBQUL79drtd//73v02oqHgkJCRowIABzgXqrujSpYt69+5tYmUFI6DcQFRUlJYuXaoJEya47F+yZIkiIyNNqsp9HnnkEefH58+f1+zZsxUZGano6GhJ0saNG7Vnzx698MILJlXofp48xHs9K1as0CeffKJ27dpp4MCBuu+++1S3bl3VqFFDixYtUp8+fcwu0S1mzJih+fPna8CAAWaXUiymT58u6fKE8Llz58rb29vZ5ufnp5o1a2ru3Llmled2devW1erVqxUfH++yf9WqVc7bzT3R5s2b9de//jXf/qpVq8rhcJhQ0Y0RUG7g1VdfVY8ePZSamqoOHTpIkhITE/X555975PyTP8/kfvbZZzV8+HC98cYb+fp48oJlnnq3yo1cbz2QIUOGmFmaW3l5ealt27Zml1FsDh8+LElq3769li1b5vF361wtISFB8fHxOnnypMvP9Lffftuj/zgpkQvUmXR7c4mycuVK45577jHKli1rVKhQwWjfvr1z3QRPZrfbjYMHD+bbf/DgQcNut5tQUfH7448/jIyMDJfNU5XW9UDefPNNY8SIEWaXgWI0e/Zso2rVqobNZjNsNptRq1Yt4+OPPza7LLcaNGiQ8cgjjxgXLlwwypcvb/z666/Gb7/9ZjRr1syyX/9MkkWBwsPDNXXq1HxD3wsXLtSYMWM8doJwaZ0sWlrXA8nLy1PXrl118OBBRUZGytfX16Xdk9b7SUhI0BtvvKFy5copISHhun09dXLwn508eVIBAQEu83A8VUZGhh577DFt2bJFZ8+eVZUqVeRwOBQdHa3vvvsu3zpXVsAlHhToxRdf1JAhQ7Rt2za1atVKkrRp0ybNnz9fr776qsnVuU9pnSw6cuRI58cxMTHav39/qVgPZPjw4Vq3bp3at2+vChUqeOTE2Cu2b9/uXOJ9+/btBfbz5H+DP7PspQ03CAoK0po1a/TTTz9p586dJWKBOkZQriEkJEQHDx5UxYoVdccdd1z3m9VT7+i44osvvtCMGTOcDw1s2LChRowYoSeeeMLkytynevXqzsmidrtd27ZtU926dfXpp5/q888/13fffWd2iUUuLy9PCxcu1LJly3TkyBHZbDbVqlVLjz32mPr16+fRv7ACAwO1ZMkS54Pz4Pm++uorffHFF0pLS8u33o+n3p1YEjGCcg3Tp0933oblyZOmbsYTTzzh0WHkWkrbZFHDMNS9e3d99913atKkiaKiomQYhvbt26cBAwZo2bJlWr58udlluk1ISIjq1KljdhkoJjNnztQrr7yiAQMG6JtvvtHAgQOVmpqqzZs3a+jQoWaXV6Su3FJ+M65euM4KGEEBrtK4cWO99957euCBBxQTE6OmTZvqrbfe0syZMzVt2jSPW8hpwYIFGjFihL755hu1b9/epW3t2rV65JFH9P7773vs07sXLFig1atXa8GCBSpbtqzZ5bhVjx49tHDhQtntdvXo0eO6fcuXL69GjRrp+eefv+a6ISVVgwYNNHHiRD311FMKDAzUjh07VLt2bU2YMEGnT5/W+++/b3aJRaZWrVo31c9ms+nXX391czWFR0C5hsIswObJy3/n5uZq+vTpBQ6FeurlrYImi164cEHTp0/3uMminTp1UocOHTR27Nhrtk+ePFlJSUn6/vvvi7my4tGsWTOlpqbKMAzVrFkz3yRZTxryHzhwoGbOnKnAwMAbPmsrJydHycnJioqK0t/+9rdiqtD9ypYtq3379qlGjRoKDQ3VmjVr1KRJE/3jH/9QmzZtrjkxHubgEs81BAcH3/Cau2EYstlsHntHh3T56ZcfffSRXnrpJY0fP16vvPKKjhw5ouXLl+dbuM6TFDRZtF69evkeeeAJdu7cqWnTphXYHhcXV6ih4pLmz4sTero/P+DzZh72uXfvXt19993uLKnYhYeH6/Tp06pRo4aqV6+ujRs3qkmTJjp8+LBKw9/rFy5c0OHDh1WnTh35+Fg7AjCCcg1JSUk31W/Xrl35ViP0JHXq1NHMmTPVtWtXBQYGKiUlxblv48aNWrx4sdklFqm1a9cqPj5eGzduzDcylpGRoXvuuUdz587VfffdZ1KF7uHn56fffvtNlStXvmb7sWPHVKtWLeXk5BRzZTBbbm6udu/erSZNmphdSpF59tlnFRERoYkTJ2rWrFkaNWqU2rZtqy1btqhHjx6aN2+e2SW6xblz5zRs2DB9/PHHki4v0Fa7dm0NGzZMVatWLXAE1UwElEI6e/asPv/8c3300UfaunWrR4+glCtXTvv27VP16tVVuXJlffvtt2revLl+/fVXNWvWTBkZGWaXWKS6d++u9u3bu4yg/NnMmTO1bt06ff3118VcmXt5e3vL4XAUeMtlenq6qlSp4tFf61dkZWXle1igp17Gbd++/XVHiteuXVuM1RSfvLw85eXlOUcPlixZog0bNqhevXr6j//4j2s+e80TjBgxQj///LPeffddde7cWTt37lTt2rX1zTff6LXXXrvubedmsfb4joWsX79e8+bN03//93+rSpUq6tGjh2bNmmV2WW5VrVo1HT9+XNWrV1edOnX097//Xc2bN9fmzZvl7+9vdnlFbseOHXrzzTcLbO/UqZPeeuutYqyoeBiGoQEDBhT4f+rpIyeHDx9WfHy8fvzxR50/f96539Mv4zZt2tTl84sXLyolJUW7d+/22Mc9XLp0SZMnT9YzzzyjatWqSZJ69eqlXr16mVyZ+y1fvlxLly5VmzZtXIJpo0aNlJqaamJlBSOgXIfD4dDChQs1b948ZWZm6oknnlBOTo6WL1/ukQ8KvNqjjz6qxMREtW7dWsOGDVPfvn01b948paWlFTjKUJKlp6fnmyD5Zz4+Pjp58mQxVlQ8buaXkafewSNJffv2lWEYmj9/fr6nGXuyKw8NvNprr72mrKysYq6mePj4+GjatGke/fVckJMnTyo0NDTf/uzsbMt+zRNQCtCtWzetX79eXbt2dQ6JeXt7e/RTPq/251VTn3zySdWoUcM5FNqtWzcTK3OPqlWravfu3apbt+4123fu3FngPI2S7GYmS3qyHTt2aOvWrapfv77ZpVhC37591apVK48cLZSkjh07KikpSTVr1jS7lGLVsmVLffvttxo2bJik/1st+KOPPnI+rd5qCCgFWLVqlYYPH64hQ4aoXr16ZpdjilOnTqlChQqSpKNHj+q7777TH3/8oZYtW5pcmXt06dJFr776qjp37qwyZcq4tP3xxx+aOHGiHnroIZOqg7vcfffdOnr0KAHlfyUnJ+f7+vckcXFxGjt2rHbt2qUWLVrkewZN9+7dTarMvSZPnqy4uDjt3btXly5d0owZM7R3715t2LDhpm8MKW5Mki3Axo0bNW/ePC1dulQNGzZUv3791KtXL1WuXFk7duzw6Es8u3btUrdu3XT06FHVq1dPS5YsUefOnZWdnS0vLy9lZ2frq6++8rjbM9PT09W8eXN5e3srPj7e+Qtr//79mjVrlnJzc7Vt2zaFhYWZXCmKUmpqqp5//nn17dtXd911V77LfJ76HKKrF2ozDEPHjx/Xli1b9Oqrr2rixIkmVeZeXl5eBbZ58pwj6fLX+tSpU7Vjxw7ns3jGjBlj2eUTCCg3kJ2draVLl2r+/Pn65ZdflJubq3feeUfPPPOMczl8TxMXFycfHx+NHTtWn376qVauXKnY2Fh9+OGHkqRhw4Zp69at2rhxo8mVFr3ffvtNQ4YM0ffff+9cE8Fmsyk2NlazZs266ZUZUXJs3LhRvXv31pEjR5z7bDabx06S/fXXX1WzZk0NGjTIZb+Xl5cqVaqkDh06qFOnTiZVh6J2swuPWvFuNQJKIRw4cEDz5s3Tp59+qjNnzujBBx/0qBUWr6hYsaLWrl2rxo0bKysrS3a7XZs3b1aLFi0kXR5RaNOmjc6cOWNuoW70+++/69ChQzIMQ/Xq1dMdd9xhdklwk8jISDVs2FCjR4++5iTZGjVqmFSZe3h7e+v48ePOCZNPPvmkZs6c6fEjg3/88YcSExOdl2nHjRvncoeaj4+PJk2a5HGXt7y8vK47CdbKQZyAcgtyc3O1YsUKzZ8/3yMDipeXlxwOh/MH2J+fVyGVrnUx4PnKlSunHTt2FDg52tNc/f1tt9uVkpLi/P72VHPnztW3336rFStWSLr8c61Ro0YKCAiQdPkPr1GjRikhIcHMMovcn+eXGIahLl266KOPPlLVqlVd+j3wwAPFXdoNMUn2Fnh7e+uRRx7xuDkYf3Z14rbqbWjA7erQoUOpCihXKy1/oy5atEijR4922bd48WJnMPvss880a9YsjwsoVwcPb29vtWnTpkQEUgIKrunPC3edP39ezz//vHO2u6cv3IXSpVu3bho5cqR27dqlqKiofJNkPe2uDpvNVir/ADl06JDLZNAyZcq4TJht1aqVhg4dakZpKACXeJDPjZ5yekVpXz8DnqG03dXh5eWluLg45x8gK1asUIcOHfLdbrts2TIzynObgIAApaSkFHg7+f79+9W0aVOX1YQ90dWX7K2MERTkQ/BAaXL1s3c83dUrB/ft29ekSopXtWrVtHv37gIDys6dO53L33u6kjJixggKgFKpS5cu+vzzzxUUFCTp8srJzz//vIKDgyVdXqjwvvvu0969e02sEkVlxIgR+uGHH7R169ZrLsTYsmVLxcTEaMaMGSZV6B5Xr3dTkkbMCCgASqWrb7e9+m4W7lbzLOnp6WratKn8/PwUHx+vO++8U9Ll5SPef/99Xbp0Sdu3b/e4261L8iV7LvEAKJWu/tuMv9U8W1hYmDZs2KAhQ4Zo7NixLgsxPvjgg5o9e7bHhRPJmsHjZhFQAAClQq1atbR69WqdPn1ahw4dkiTVrVtXISEhJleGayGgACiVSuvttpBCQkLUqlUrs8vADRBQAJRKhmGw3g9gYUySBVAqleTJg0BpQEABAACWU/ASigAAACYhoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMv5/7k7B54TiZ/jAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1218,7 +1235,7 @@
     "import pyomo.environ as pyo\n",
     "\n",
     "\n",
-    "def transport(supply, demand, rates):\n",
+    "def transport_v3(supply, demand, rates):\n",
     "    m = pyo.ConcreteModel()\n",
     "\n",
     "    m.SOURCES = pyo.Set(initialize=rates.columns)\n",
@@ -1267,7 +1284,7 @@
     "    return m\n",
     "\n",
     "\n",
-    "m = transport(supply, demand, rates / 100)\n",
+    "m = transport_v3(supply, demand, rates / 100)\n",
     "\n",
     "results = round(\n",
     "    pd.DataFrame(\n",
@@ -1282,13 +1299,13 @@
     "results[\"savings\"] = results[\"current costs\"].round(1) - results[\n",
     "    \"contract costs\"\n",
     "].round(1)\n",
-    "results[\"contract rate\"] = round(results[\"contract costs\"] / demand, 4)\n",
-    "results[\"marginal cost\"] = pd.Series(\n",
+    "results[\"contract rate\"] = 100 * round(results[\"contract costs\"] / demand, 4)\n",
+    "results[\"marginal cost\"] = 100 * pd.Series(\n",
     "    {dst: m.dual[m.demand_constraint[dst]] for dst in m.DESTINATIONS}\n",
     ")\n",
     "\n",
-    "print(f\"Old Delivery Costs = {sum(demand)*700/8000:.2f}€\")\n",
-    "print(f\"New Delivery Costs = {m.total_cost():.2f}€\\n\")\n",
+    "print(f\"Old delivery costs = ${sum(demand)*700/8000:.2f}\")\n",
+    "print(f\"New delivery costs = ${m.total_cost():.2f}\\n\")\n",
     "display(results)\n",
     "\n",
     "results.plot(y=\"savings\", kind=\"bar\")\n",
@@ -1306,12 +1323,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x300 with 2 Axes>"
       ]
@@ -1332,6 +1349,7 @@
     "model3_results.plot(y=\"savings\", kind=\"bar\", ax=ax[0], color=\"r\", alpha=alpha)\n",
     "model3_results.plot(y=\"savings\", marker=\"o\", ax=ax[0], color=\"r\", alpha=alpha)\n",
     "ax[0].legend([\"Model 1\", \"Model 3\"])\n",
+    "ax[0].set_ylim(0, 470)\n",
     "ax[0].set_title(\"Delivery costs by franchise\")\n",
     "\n",
     "model1_results.plot(y=[\"contract rate\"], kind=\"bar\", ax=ax[1], color=\"g\", alpha=alpha)\n",
@@ -1339,7 +1357,7 @@
     "\n",
     "model3_results.plot(y=\"contract rate\", kind=\"bar\", ax=ax[1], color=\"r\", alpha=alpha)\n",
     "model3_results.plot(y=\"contract rate\", marker=\"o\", ax=ax[1], color=\"r\", alpha=alpha)\n",
-    "ax[1].set_ylim(0.07, 0.09)\n",
+    "ax[1].set_ylim(7.25, 9)\n",
     "ax[1].legend([\"Model 1\", \"Model 3\"])\n",
     "ax[1].set_title(\"Contract rates by franchise\")\n",
     "plt.show()"
@@ -1371,7 +1389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1412,7 +1430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1472,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1516,7 +1534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1554,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1591,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1641,7 +1659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1712,7 +1730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1741,47 +1759,47 @@
       "cost = 26113.5\n",
       "\n",
       "Constraint: supply_constraint\n",
-      "Terminal A    100000.00     -0.00\n",
-      "Terminal B    80000.00     -0.01\n",
-      "Current Supplier  133000.00      0.00\n",
+      "Terminal A      100000     -0.00\n",
+      "Terminal B       80000     -0.01\n",
+      "Current Supplier    133000      0.00\n",
       "\n",
       "Constraint: demand_constraint\n",
-      "Alice         30000.00      0.09\n",
-      "Badri         40000.00      0.09\n",
-      "Cara          50000.00      0.09\n",
-      "Dan           20000.00      0.09\n",
-      "Emma          30000.00      0.09\n",
-      "Fujita        45000.00      0.09\n",
-      "Grace         80000.00      0.09\n",
-      "Helen         18000.00      0.08\n",
+      "Alice            30000      0.09\n",
+      "Badri            40000      0.09\n",
+      "Cara             50000      0.09\n",
+      "Dan              20000      0.09\n",
+      "Emma             30000      0.09\n",
+      "Fujita           45000      0.09\n",
+      "Grace            80000      0.09\n",
+      "Helen            18000      0.08\n",
       "\n",
       "Decision variables: x\n",
-      "Terminal A   -> Alice         30000.00\n",
-      "Terminal A   -> Badri         40000.00\n",
-      "Terminal A   -> Cara          12000.00\n",
-      "Terminal A   -> Dan               0.00\n",
-      "Terminal A   -> Emma              0.00\n",
-      "Terminal A   -> Fujita            0.00\n",
-      "Terminal A   -> Grace             0.00\n",
-      "Terminal A   -> Helen         18000.00\n",
+      "Terminal A   -> Alice            30000\n",
+      "Terminal A   -> Badri            40000\n",
+      "Terminal A   -> Cara             12000\n",
+      "Terminal A   -> Dan                  0\n",
+      "Terminal A   -> Emma                 0\n",
+      "Terminal A   -> Fujita               0\n",
+      "Terminal A   -> Grace                0\n",
+      "Terminal A   -> Helen            18000\n",
       "\n",
-      "Terminal B   -> Alice             0.00\n",
-      "Terminal B   -> Badri             0.00\n",
-      "Terminal B   -> Cara              0.00\n",
-      "Terminal B   -> Dan           20000.00\n",
-      "Terminal B   -> Emma              0.00\n",
-      "Terminal B   -> Fujita            0.00\n",
-      "Terminal B   -> Grace         60000.00\n",
-      "Terminal B   -> Helen             0.00\n",
+      "Terminal B   -> Alice                0\n",
+      "Terminal B   -> Badri                0\n",
+      "Terminal B   -> Cara                 0\n",
+      "Terminal B   -> Dan              20000\n",
+      "Terminal B   -> Emma                 0\n",
+      "Terminal B   -> Fujita               0\n",
+      "Terminal B   -> Grace            60000\n",
+      "Terminal B   -> Helen                0\n",
       "\n",
-      "Current Supplier -> Alice             0.00\n",
-      "Current Supplier -> Badri             0.00\n",
-      "Current Supplier -> Cara          38000.00\n",
-      "Current Supplier -> Dan               0.00\n",
-      "Current Supplier -> Emma          30000.00\n",
-      "Current Supplier -> Fujita        45000.00\n",
-      "Current Supplier -> Grace         20000.00\n",
-      "Current Supplier -> Helen             0.00\n",
+      "Current Supplier -> Alice                0\n",
+      "Current Supplier -> Badri                0\n",
+      "Current Supplier -> Cara             38000\n",
+      "Current Supplier -> Dan                  0\n",
+      "Current Supplier -> Emma             30000\n",
+      "Current Supplier -> Fujita           45000\n",
+      "Current Supplier -> Grace            20000\n",
+      "Current Supplier -> Helen                0\n",
       "\n"
      ]
     }
@@ -1795,20 +1813,20 @@
     "print(\"\\nConstraint: supply_constraint\")\n",
     "for src in m.SOURCES:\n",
     "    print(\n",
-    "        f\"{src:12s}  {m.supply_constraint[src]():8.2f}  {m.dual[m.supply_constraint[src]]:8.2f}\"\n",
+    "        f\"{src:12s}  {m.supply_constraint[src]():8.0f}  {m.dual[m.supply_constraint[src]]:8.2f}\"\n",
     "    )\n",
     "\n",
     "print(\"\\nConstraint: demand_constraint\")\n",
     "for dst in m.DESTINATIONS:\n",
     "    print(\n",
-    "        f\"{dst:12s}  {m.demand_constraint[dst]():8.2f}  {m.dual[m.demand_constraint[dst]]:8.2f}\"\n",
+    "        f\"{dst:12s}  {m.demand_constraint[dst]():8.0f}  {m.dual[m.demand_constraint[dst]]:8.2f}\"\n",
     "    )\n",
     "\n",
     "# Decision variable reports\n",
     "print(\"\\nDecision variables: x\")\n",
     "for src in m.SOURCES:\n",
     "    for dst in m.DESTINATIONS:\n",
-    "        print(f\"{src:12s} -> {dst:12s}  {m.x[dst, src]():8.2f}\")\n",
+    "        print(f\"{src:12s} -> {dst:12s}  {m.x[dst, src]():8.0f}\")\n",
     "    print()"
    ]
   },
@@ -1825,7 +1843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2095,7 +2113,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2136,6 +2154,7 @@
     ").T\n",
     "display(shipments)\n",
     "shipments.plot(kind=\"bar\")\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -2148,12 +2167,12 @@
    "source": [
     "### Graphviz\n",
     "\n",
-    "The `graphviz` utility is a collection of tools for visually graphs and directed graphs. Unfortunately, the package can be troublesome to install on laptops in a way that is compatible with many JupyterLab installations. Accordingly, the following cell is intended for use on Google Colab which provides a preinstalled version of `graphviz`."
+    "The `graphviz` utility is a collection of tools for visually graphs and directed graphs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2173,56 +2192,247 @@
     "id": "etWChv-k0VyS",
     "outputId": "9f52151f-6593-465f-d8a3-e5ef62aff814"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 9.0.0 (20230911.1827)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"1038pt\" height=\"173pt\"\n",
+       " viewBox=\"0.00 0.00 1037.75 172.50\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 168.5)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-168.5 1033.75,-168.5 1033.75,4 -4,4\"/>\n",
+       "<!-- Terminal A -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>Terminal A</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"261.12,-164.5 163.38,-164.5 163.38,-111.5 261.12,-111.5 261.12,-164.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"212.25\" y=\"-151\" font-family=\"Times,serif\" font-size=\"10.00\">Terminal A</text>\n",
+       "<text text-anchor=\"middle\" x=\"212.25\" y=\"-139.75\" font-family=\"Times,serif\" font-size=\"10.00\">supply = 100000</text>\n",
+       "<text text-anchor=\"middle\" x=\"212.25\" y=\"-128.5\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 100000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"212.25\" y=\"-117.25\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= &#45;0.0045</text>\n",
+       "</g>\n",
+       "<!-- Alice -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>Alice</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"92.5,-53 0,-53 0,0 92.5,0 92.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"46.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Alice</text>\n",
+       "<text text-anchor=\"middle\" x=\"46.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 30000</text>\n",
+       "<text text-anchor=\"middle\" x=\"46.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 30000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"46.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Terminal A&#45;&gt;Alice -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>Terminal A&#45;&gt;Alice</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M162.92,-131.1C134.84,-125.5 100.62,-114.62 76.75,-93.5 67.89,-85.66 61.42,-74.71 56.79,-63.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"60.14,-62.94 53.29,-54.86 53.61,-65.45 60.14,-62.94\"/>\n",
+       "<text text-anchor=\"middle\" x=\"115.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.3</text>\n",
+       "<text text-anchor=\"middle\" x=\"115.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 30000.0</text>\n",
+       "</g>\n",
+       "<!-- Badri -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>Badri</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"203.5,-53 111,-53 111,0 203.5,0 203.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"157.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Badri</text>\n",
+       "<text text-anchor=\"middle\" x=\"157.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 40000</text>\n",
+       "<text text-anchor=\"middle\" x=\"157.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 40000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"157.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0855</text>\n",
+       "</g>\n",
+       "<!-- Terminal A&#45;&gt;Badri -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>Terminal A&#45;&gt;Badri</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M176.85,-111.19C171.69,-105.9 167.02,-99.96 163.75,-93.5 159.35,-84.81 157.15,-74.66 156.17,-64.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"159.66,-64.77 155.61,-54.98 152.68,-65.17 159.66,-64.77\"/>\n",
+       "<text text-anchor=\"middle\" x=\"202.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.1</text>\n",
+       "<text text-anchor=\"middle\" x=\"202.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 40000.0</text>\n",
+       "</g>\n",
+       "<!-- Cara -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>Cara</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"461.5,-53 369,-53 369,0 461.5,0 461.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"415.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Cara</text>\n",
+       "<text text-anchor=\"middle\" x=\"415.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 50000</text>\n",
+       "<text text-anchor=\"middle\" x=\"415.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 50000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"415.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Terminal A&#45;&gt;Cara -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>Terminal A&#45;&gt;Cara</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M261.54,-122.86C283.58,-115.58 309.44,-105.65 331.25,-93.5 347.6,-84.39 364.11,-72.21 378.11,-60.8\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"380.33,-63.51 385.77,-54.41 375.84,-58.13 380.33,-63.51\"/>\n",
+       "<text text-anchor=\"middle\" x=\"401.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.3</text>\n",
+       "<text text-anchor=\"middle\" x=\"401.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 12000.0</text>\n",
+       "</g>\n",
+       "<!-- Helen -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>Helen</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"314.5,-53 222,-53 222,0 314.5,0 314.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"268.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Helen</text>\n",
+       "<text text-anchor=\"middle\" x=\"268.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 18000</text>\n",
+       "<text text-anchor=\"middle\" x=\"268.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 18000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"268.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0795</text>\n",
+       "</g>\n",
+       "<!-- Terminal A&#45;&gt;Helen -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>Terminal A&#45;&gt;Helen</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M229.87,-111.21C233.49,-105.49 237.14,-99.37 240.25,-93.5 245.2,-84.15 249.95,-73.73 254.08,-63.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"257.31,-65.34 257.89,-54.76 250.84,-62.66 257.31,-65.34\"/>\n",
+       "<text text-anchor=\"middle\" x=\"289.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 7.5</text>\n",
+       "<text text-anchor=\"middle\" x=\"289.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 18000.0</text>\n",
+       "</g>\n",
+       "<!-- Terminal B -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>Terminal B</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"963.5,-164.5 871,-164.5 871,-111.5 963.5,-111.5 963.5,-164.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"917.25\" y=\"-151\" font-family=\"Times,serif\" font-size=\"10.00\">Terminal B</text>\n",
+       "<text text-anchor=\"middle\" x=\"917.25\" y=\"-139.75\" font-family=\"Times,serif\" font-size=\"10.00\">supply = 80000</text>\n",
+       "<text text-anchor=\"middle\" x=\"917.25\" y=\"-128.5\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 80000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"917.25\" y=\"-117.25\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= &#45;0.0075</text>\n",
+       "</g>\n",
+       "<!-- Dan -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>Dan</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"1013.5,-53 921,-53 921,0 1013.5,0 1013.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"967.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Dan</text>\n",
+       "<text text-anchor=\"middle\" x=\"967.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 20000</text>\n",
+       "<text text-anchor=\"middle\" x=\"967.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 20000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"967.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Terminal B&#45;&gt;Dan -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>Terminal B&#45;&gt;Dan</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M935.23,-111.39C938.83,-105.66 942.38,-99.49 945.25,-93.5 949.64,-84.34 953.52,-74.08 956.75,-64.42\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"960.08,-65.5 959.78,-54.91 953.41,-63.38 960.08,-65.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"991.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"991.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 20000.0</text>\n",
+       "</g>\n",
+       "<!-- Grace -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>Grace</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"863.5,-53 771,-53 771,0 863.5,0 863.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"817.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Grace</text>\n",
+       "<text text-anchor=\"middle\" x=\"817.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 80000</text>\n",
+       "<text text-anchor=\"middle\" x=\"817.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 80000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"817.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Terminal B&#45;&gt;Grace -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>Terminal B&#45;&gt;Grace</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M883.34,-111.07C876.89,-105.55 870.41,-99.55 864.75,-93.5 855.97,-84.1 847.42,-73.03 840.06,-62.67\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"843.08,-60.9 834.51,-54.67 837.33,-64.88 843.08,-60.9\"/>\n",
+       "<text text-anchor=\"middle\" x=\"902.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"902.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 60000.0</text>\n",
+       "</g>\n",
+       "<!-- Current Supplier -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>Current Supplier</title>\n",
+       "<polygon fill=\"lightblue\" stroke=\"black\" points=\"663.12,-164.5 565.38,-164.5 565.38,-111.5 663.12,-111.5 663.12,-164.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"614.25\" y=\"-151\" font-family=\"Times,serif\" font-size=\"10.00\">Current Supplier</text>\n",
+       "<text text-anchor=\"middle\" x=\"614.25\" y=\"-139.75\" font-family=\"Times,serif\" font-size=\"10.00\">supply = 500000</text>\n",
+       "<text text-anchor=\"middle\" x=\"614.25\" y=\"-128.5\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 133000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"614.25\" y=\"-117.25\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0</text>\n",
+       "</g>\n",
+       "<!-- Current Supplier&#45;&gt;Cara -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>Current Supplier&#45;&gt;Cara</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M565,-126.65C538.98,-119.79 507.25,-109.11 481.75,-93.5 467.8,-84.96 454.54,-72.94 443.59,-61.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"446.43,-59.41 437.07,-54.43 441.28,-64.15 446.43,-59.41\"/>\n",
+       "<text text-anchor=\"middle\" x=\"520.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.75</text>\n",
+       "<text text-anchor=\"middle\" x=\"520.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 38000.0</text>\n",
+       "</g>\n",
+       "<!-- Emma -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>Emma</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"716.5,-53 624,-53 624,0 716.5,0 716.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"670.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Emma</text>\n",
+       "<text text-anchor=\"middle\" x=\"670.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 30000</text>\n",
+       "<text text-anchor=\"middle\" x=\"670.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 30000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"670.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Current Supplier&#45;&gt;Emma -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>Current Supplier&#45;&gt;Emma</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M634.4,-111.01C638.28,-105.41 642.1,-99.39 645.25,-93.5 650.2,-84.23 654.64,-73.77 658.35,-63.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"661.6,-65.26 661.71,-54.67 655.02,-62.88 661.6,-65.26\"/>\n",
+       "<text text-anchor=\"middle\" x=\"693.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.75</text>\n",
+       "<text text-anchor=\"middle\" x=\"693.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 30000.0</text>\n",
+       "</g>\n",
+       "<!-- Fujita -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>Fujita</title>\n",
+       "<polygon fill=\"gold\" stroke=\"black\" points=\"605.5,-53 513,-53 513,0 605.5,0 605.5,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"559.25\" y=\"-39.5\" font-family=\"Times,serif\" font-size=\"10.00\">Fujita</text>\n",
+       "<text text-anchor=\"middle\" x=\"559.25\" y=\"-28.25\" font-family=\"Times,serif\" font-size=\"10.00\">demand = 45000</text>\n",
+       "<text text-anchor=\"middle\" x=\"559.25\" y=\"-17\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 45000.0</text>\n",
+       "<text text-anchor=\"middle\" x=\"559.25\" y=\"-5.75\" font-family=\"Times,serif\" font-size=\"10.00\">sens &#160;= 0.0875</text>\n",
+       "</g>\n",
+       "<!-- Current Supplier&#45;&gt;Fujita -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>Current Supplier&#45;&gt;Fujita</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M581.72,-111.28C576.67,-105.91 572.05,-99.91 568.75,-93.5 564.22,-84.7 561.65,-74.41 560.25,-64.61\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"563.76,-64.54 559.24,-54.96 556.8,-65.27 563.76,-64.54\"/>\n",
+       "<text text-anchor=\"middle\" x=\"607.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.75</text>\n",
+       "<text text-anchor=\"middle\" x=\"607.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 45000.0</text>\n",
+       "</g>\n",
+       "<!-- Current Supplier&#45;&gt;Grace -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>Current Supplier&#45;&gt;Grace</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M663.56,-123.38C686.17,-116.12 712.86,-106.06 735.25,-93.5 740.13,-90.76 759.14,-75.51 777.63,-60.36\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"779.64,-63.23 785.15,-54.18 775.2,-57.83 779.64,-63.23\"/>\n",
+       "<text text-anchor=\"middle\" x=\"802.5\" y=\"-84\" font-family=\"Times,serif\" font-size=\"10.00\">rate = 8.75</text>\n",
+       "<text text-anchor=\"middle\" x=\"802.5\" y=\"-72.75\" font-family=\"Times,serif\" font-size=\"10.00\">shipped = 20000.0</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x14788feb0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "import sys\n",
-    "\n",
+    "from graphviz import Digraph\n",
     "\n",
-    "if \"google.colab\" in sys.modules:\n",
-    "    import graphviz\n",
-    "    from graphviz import Digraph\n",
+    "dot = Digraph(\n",
+    "    node_attr={\"fontsize\": \"10\", \"shape\": \"rectangle\", \"style\": \"filled\"},\n",
+    "    edge_attr={\"fontsize\": \"10\"},\n",
+    ")\n",
     "\n",
-    "    dot = Digraph(\n",
-    "        node_attr={\"fontsize\": \"10\", \"shape\": \"rectangle\", \"style\": \"filled\"},\n",
-    "        edge_attr={\"fontsize\": \"10\"},\n",
+    "for src in m.SOURCES:\n",
+    "    label = (\n",
+    "        f\"{src}\"\n",
+    "        + f\"\\nsupply = {supply[src]}\"\n",
+    "        + f\"\\nshipped = {m.supply_constraint[src]()}\"\n",
+    "        + f\"\\nsens  = {m.dual[m.supply_constraint[src]]}\"\n",
     "    )\n",
+    "    dot.node(src, label=label, fillcolor=\"lightblue\")\n",
     "\n",
-    "    for src in m.SOURCES:\n",
-    "        label = (\n",
-    "            f\"{src}\"\n",
-    "            + f\"\\nsupply = {supply[src]}\"\n",
-    "            + f\"\\nshipped = {m.supply_constraint[src]()}\"\n",
-    "            + f\"\\nsens  = {m.dual[m.supply_constraint[src]]}\"\n",
-    "        )\n",
-    "        dot.node(src, label=label, fillcolor=\"lightblue\")\n",
+    "for dst in m.DESTINATIONS:\n",
+    "    label = (\n",
+    "        f\"{dst}\"\n",
+    "        + f\"\\ndemand = {demand[dst]}\"\n",
+    "        + f\"\\nshipped = {m.demand_constraint[dst]()}\"\n",
+    "        + f\"\\nsens  = {m.dual[m.demand_constraint[dst]]}\"\n",
+    "    )\n",
+    "    dot.node(dst, label=label, fillcolor=\"gold\")\n",
     "\n",
+    "for src in m.SOURCES:\n",
     "    for dst in m.DESTINATIONS:\n",
-    "        label = (\n",
-    "            f\"{dst}\"\n",
-    "            + f\"\\ndemand = {demand[dst]}\"\n",
-    "            + f\"\\nshipped = {m.demand_constraint[dst]()}\"\n",
-    "            + f\"\\nsens  = {m.dual[m.demand_constraint[dst]]}\"\n",
-    "        )\n",
-    "        dot.node(dst, label=label, fillcolor=\"gold\")\n",
-    "\n",
-    "    for src in m.SOURCES:\n",
-    "        for dst in m.DESTINATIONS:\n",
-    "            if m.x[dst, src]() > 0:\n",
-    "                dot.edge(\n",
-    "                    src,\n",
-    "                    dst,\n",
-    "                    f\"rate = {rates.loc[dst, src]}\\nshipped = {m.x[dst, src]()}\",\n",
-    "                )\n",
-    "\n",
-    "    display(dot)"
+    "        if m.x[dst, src]() > 0:\n",
+    "            dot.edge(\n",
+    "                src,\n",
+    "                dst,\n",
+    "                f\"rate = {rates.loc[dst, src]}\\nshipped = {m.x[dst, src]()}\",\n",
+    "            )\n",
+    "\n",
+    "display(dot)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2241,7 +2451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/genindex.html b/genindex.html
index 293d8858..c975514c 100644
--- a/genindex.html
+++ b/genindex.html
@@ -666,7 +666,7 @@ <h2 id="S">S</h2>
     solver
 
       <ul>
-        <li><a href="notebooks/01/production-planning-advanced.html#index-4">cbc</a>, <a href="notebooks/01/production-planning-basic.html#index-5">[1]</a>, <a href="notebooks/01/production-planning.html#index-4">[2]</a>, <a href="notebooks/02/L1-regression-wine-quality.html#index-3">[3]</a>, <a href="notebooks/02/bim-dual.html#index-1">[4]</a>, <a href="notebooks/02/bim-fractional.html#index-0">[5]</a>, <a href="notebooks/02/bim-maxmin.html#index-0">[6]</a>, <a href="notebooks/02/bim-rawmaterialplanning.html#index-2">[7]</a>, <a href="notebooks/02/bim.html#index-1">[8]</a>, <a href="notebooks/02/mad-portfolio-optimization.html#index-2">[9]</a>, <a href="notebooks/02/multiproductionfaciliity_worstcase.html#index-3">[10]</a>, <a href="notebooks/03/bim-perturbed.html#index-0">[11]</a>, <a href="notebooks/03/bim-production-revisited.html#index-3">[12]</a>, <a href="notebooks/03/job-shop-scheduling.html#index-4">[13]</a>, <a href="notebooks/03/machine-scheduling.html#index-2">[14]</a>, <a href="notebooks/03/maintenance-planning.html#index-1">[15]</a>, <a href="notebooks/03/recharging-electric-vehicle.html#index-2">[16]</a>, <a href="notebooks/03/shift-scheduling.html#index-2">[17]</a>, <a href="notebooks/04/cryptocurrency-arbitrage.html#index-1">[18]</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-1">[19]</a>, <a href="notebooks/04/gasoline-distribution.html#index-1">[20]</a>, <a href="notebooks/04/power-network.html#index-1">[21]</a>, <a href="notebooks/05/milk-pooling.html#index-5">[22]</a>, <a href="notebooks/05/refinery-production.html#index-0">[23]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-1">[24]</a>, <a href="notebooks/07/fleet-assignment.html#index-1">[25]</a>, <a href="notebooks/09/economicdispatch.html#index-0">[26]</a>, <a href="notebooks/09/pop-up_shop.html#index-1">[27]</a>, <a href="notebooks/09/seafood.html#index-1">[28]</a>, <a href="notebooks/10/airline-seating.html#index-1">[29]</a>, <a href="notebooks/10/ccg.html#index-2">[30]</a>, <a href="notebooks/10/farmer.html#index-1">[31]</a>, <a href="notebooks/10/opf-ldr.html#index-1">[32]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-1">[33]</a>
+        <li><a href="notebooks/01/production-planning-advanced.html#index-4">cbc</a>, <a href="notebooks/01/production-planning-basic.html#index-5">[1]</a>, <a href="notebooks/01/production-planning.html#index-4">[2]</a>, <a href="notebooks/02/L1-regression-wine-quality.html#index-3">[3]</a>, <a href="notebooks/02/bim-dual.html#index-1">[4]</a>, <a href="notebooks/02/bim-fractional.html#index-0">[5]</a>, <a href="notebooks/02/bim-maxmin.html#index-0">[6]</a>, <a href="notebooks/02/bim-rawmaterialplanning.html#index-2">[7]</a>, <a href="notebooks/02/bim.html#index-1">[8]</a>, <a href="notebooks/02/mad-portfolio-optimization.html#index-2">[9]</a>, <a href="notebooks/02/multiproductionfaciliity_worstcase.html#index-3">[10]</a>, <a href="notebooks/03/bim-perturbed.html#index-0">[11]</a>, <a href="notebooks/03/bim-production-revisited.html#index-3">[12]</a>, <a href="notebooks/03/job-shop-scheduling.html#index-4">[13]</a>, <a href="notebooks/03/machine-scheduling.html#index-2">[14]</a>, <a href="notebooks/03/maintenance-planning.html#index-1">[15]</a>, <a href="notebooks/03/recharging-electric-vehicle.html#index-2">[16]</a>, <a href="notebooks/03/shift-scheduling.html#index-2">[17]</a>, <a href="notebooks/04/cryptocurrency-arbitrage.html#index-1">[18]</a>, <a href="notebooks/04/dinner-seat-allocation.html#index-1">[19]</a>, <a href="notebooks/04/power-network.html#index-1">[20]</a>, <a href="notebooks/05/milk-pooling.html#index-5">[21]</a>, <a href="notebooks/05/refinery-production.html#index-0">[22]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-1">[23]</a>, <a href="notebooks/07/fleet-assignment.html#index-1">[24]</a>, <a href="notebooks/09/economicdispatch.html#index-0">[25]</a>, <a href="notebooks/09/pop-up_shop.html#index-1">[26]</a>, <a href="notebooks/09/seafood.html#index-1">[27]</a>, <a href="notebooks/10/airline-seating.html#index-1">[28]</a>, <a href="notebooks/10/ccg.html#index-2">[29]</a>, <a href="notebooks/10/farmer.html#index-1">[30]</a>, <a href="notebooks/10/opf-ldr.html#index-1">[31]</a>, <a href="notebooks/10/opf-wind-curtailment.html#index-1">[32]</a>
 </li>
         <li><a href="notebooks/05/milk-pooling.html#index-6">couenne</a>
 </li>
@@ -674,7 +674,7 @@ <h2 id="S">S</h2>
 </li>
         <li><a href="notebooks/05/refinery-production.html#index-1">cvxpy</a>
 </li>
-        <li><a href="notebooks/02/bim-fractional.html#index-1">highs</a>, <a href="notebooks/02/bim-maxmin.html#index-1">[1]</a>, <a href="notebooks/02/bim-rawmaterialplanning.html#index-3">[2]</a>, <a href="notebooks/02/bim.html#index-2">[3]</a>, <a href="notebooks/02/mad-portfolio-optimization.html#index-3">[4]</a>, <a href="notebooks/02/multiproductionfaciliity_worstcase.html#index-4">[5]</a>, <a href="notebooks/04/mincost-flow.html#index-1">[6]</a>, <a href="notebooks/05/refinery-production.html#index-2">[7]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-2">[8]</a>, <a href="notebooks/07/fleet-assignment.html#index-2">[9]</a>
+        <li><a href="notebooks/02/bim-fractional.html#index-1">highs</a>, <a href="notebooks/02/bim-maxmin.html#index-1">[1]</a>, <a href="notebooks/02/bim-rawmaterialplanning.html#index-3">[2]</a>, <a href="notebooks/02/bim.html#index-2">[3]</a>, <a href="notebooks/02/mad-portfolio-optimization.html#index-3">[4]</a>, <a href="notebooks/02/multiproductionfaciliity_worstcase.html#index-4">[5]</a>, <a href="notebooks/04/gasoline-distribution.html#index-1">[6]</a>, <a href="notebooks/04/mincost-flow.html#index-1">[7]</a>, <a href="notebooks/05/refinery-production.html#index-2">[8]</a>, <a href="notebooks/07/bim-robustness-analysis.html#index-2">[9]</a>, <a href="notebooks/07/fleet-assignment.html#index-2">[10]</a>
 </li>
         <li><a href="notebooks/06/building-insulation.html#index-3">Mosek</a>, <a href="notebooks/06/investment-wheel.html#index-3">[1]</a>, <a href="notebooks/06/kelly-criterion.html#index-3">[2]</a>
 </li>
diff --git a/notebooks/04/dinner-seat-allocation.html b/notebooks/04/dinner-seat-allocation.html
index c4ade347..d05bd1f2 100644
--- a/notebooks/04/dinner-seat-allocation.html
+++ b/notebooks/04/dinner-seat-allocation.html
@@ -1158,7 +1158,7 @@ <h1>Reformulation as max flow problem<a class="headerlink" href="#reformulation-
 <li><p>the nodes on the left-hand side stand for the tables and the numbers next to them provide the table size;</p></li>
 <li><p>each left-to-right arrow stand comes with a number denoting the capacity of arc <span class="math notranslate nohighlight">\((f, t)\)</span> – how many people of family <span class="math notranslate nohighlight">\(f\)</span> can be assigned to table <span class="math notranslate nohighlight">\(t\)</span>.</p></li>
 </ul>
-<p><img alt="" src="../../_images/seating_model_basic.png" /></p>
+<p><img alt="" src="notebooks/04/seating_model_basic.png" /></p>
 <p>If we think of each family as a “supply of individuals” and each table as a “demand of individuals”, then we can rephrase our original task as the problem of sending people from families <span class="math notranslate nohighlight">\(f\)</span> to tables <span class="math notranslate nohighlight">\(t\)</span> so that everyone is assigned to some table, the tables’ capacities are respected, and no table gets more than <span class="math notranslate nohighlight">\(k_{\max} = 3\)</span> members of the same family.</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
@@ -1303,7 +1303,7 @@ <h1>Reformulation as max flow problem<a class="headerlink" href="#reformulation-
 </div>
 </div>
 <p>By adding two more nodes to the graph above, we can formulate the problem as a slightly different flow problem where all the data is formulated as the arc capacity, see figure below. In a network like this, we can imagine a problem of sending resources from the <em>source node</em> “door” to the <em>target node</em> “seat”, subject to the restriction that for any node that is neither the source nor the target, the sum of incoming and outgoing flows are equal (<em>balance constraint</em>). If there exists a flow in this new graph that respects the arc capacities and the sum of outgoing flows at the source is equal to the total number of individuals, that is <span class="math notranslate nohighlight">\(\sum_{f \in F} m_f\)</span>, it means that there exists a family-to-table assignment that meets our requirements.</p>
-<p><img alt="" src="../../_images/seating_flow_model.png" /></p>
+<p><img alt="" src="notebooks/04/seating_flow_model.png" /></p>
 <p>Thus, if we maximize the total flow going out of <code class="docutils literal notranslate"><span class="pre">door</span></code> and reaching <code class="docutils literal notranslate"><span class="pre">seat</span></code> and it matches the total number of individuals, the problem is solved. As unimpressive as this sounds, this means that it can be treated as a special case of a famous <strong>maximum flow problem</strong>, for which there exist algorithms that are way more efficient than a generic LO solver. One such algorithm is the Bellman-Ford algorithm, implicitly invoked in the following code using the Python package <code class="docutils literal notranslate"><span class="pre">networkx</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
diff --git a/notebooks/04/gasoline-distribution.html b/notebooks/04/gasoline-distribution.html
index b593c75d..f1cfa9ba 100644
--- a/notebooks/04/gasoline-distribution.html
+++ b/notebooks/04/gasoline-distribution.html
@@ -528,19 +528,6 @@ <h2> Contents </h2>
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="gasoline-distribution">
 <span id="index-4"></span><h1>Gasoline distribution<a class="headerlink" href="#gasoline-distribution" title="Permalink to this heading">#</a></h1>
 <p>This notebook presents a transportation model to optimally allocate the delivery of a commodity from multiple sources to multiple destinations. The model invites a discussion of the pitfalls in optimizing a global objective for customers who may have an uneven share of the resulting benefits, then through model refinement arrives at a group cost-sharing plan to delivery costs.</p>
-<p>Didactically, notebook presents techniques for Pyomo modeling and reporting including:</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">pyo.Expression</span></code> decorator</p></li>
-<li><p>Accessing the duals (i.e., shadow prices)</p></li>
-<li><p>Methods for reporting the solution and duals.</p>
-<ul>
-<li><p>Pyomo <code class="docutils literal notranslate"><span class="pre">.display()</span></code> method for Pyomo objects</p></li>
-<li><p>Manually formatted reports</p></li>
-<li><p>Pandas</p></li>
-<li><p>Graphviz for display of results as a directed graph.</p></li>
-</ul>
-</li>
-</ul>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>This cell selects and verifies a global SOLVER for the notebook.</p>
@@ -551,87 +538,98 @@ <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-in
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-
+ 
 <span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pyomo<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>highspy<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
-
-    <span class="kn">from</span> <span class="nn">pyomo.environ</span> <span class="kn">import</span> <span class="n">SolverFactory</span>
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="n">SolverFactory</span><span class="p">(</span><span class="s1">&#39;appsi_highs&#39;</span><span class="p">)</span>
-    
-<span class="k">else</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">pyomo.environ</span> <span class="kn">import</span> <span class="n">SolverFactory</span>
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="n">SolverFactory</span><span class="p">(</span><span class="s1">&#39;cbc&#39;</span><span class="p">)</span>
-
-<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">SOLVER</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
+ 
+<span class="n">solver</span> <span class="o">=</span> <span class="s1">&#39;appsi_highs&#39;</span>
+ 
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="n">SOLVER</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+ 
+<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
 </pre></div>
 </div>
 </div>
 </div>
+<p>Didactically, this notebook presents techniques for Pyomo modeling and reporting including:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">pyo.Expression</span></code> decorator</p></li>
+<li><p>Accessing the duals (i.e., shadow prices)</p></li>
+<li><p>Methods for reporting the solution and duals.</p>
+<ul>
+<li><p>Pyomo <code class="docutils literal notranslate"><span class="pre">.display()</span></code> method for Pyomo objects</p></li>
+<li><p>Manually formatted reports</p></li>
+<li><p>Pandas</p></li>
+<li><p>Graphviz for display of results as a directed graph.</p></li>
+</ul>
+</li>
+</ul>
 </section>
 <section id="problem-distributing-gasoline-to-franchise-operators">
 <h2>Problem: Distributing gasoline to franchise operators<a class="headerlink" href="#problem-distributing-gasoline-to-franchise-operators" title="Permalink to this heading">#</a></h2>
-<p>YaYa Gas-n-Grub is franchiser and operator for a network of regional convenience stores selling gasoline and convenience items in the United States. Each store is individually owned by a YaYa Gas-n-Grub franchisee who pays a fee to the franchiser for services.
-Gasoline is delivered by truck from regional distribution terminals. Each delivery truck carries 8,000 gallons delivered at a fixed charge of 700€ per delivery, or 0.0875€  per gallon. The franchise owners are eager to reduce delivery costs to boost profits.
-YaYa Gas-n-Grub decides to accept proposals from other distribution terminals, “A” and “B”, to supply the franchise operators. Rather than a fixed fee per delivery, they proposed pricing based on location. But they already have existing customers, “A” and “B” can only provide a limited amount of gasoline to new customers totaling 100,000 and 80,000 gallons respectively. The only difference between the new suppliers and the current supplier is the delivery charge.</p>
+<p>YaYa Gas-n-Grub is the franchisor and operator of a network of regional convenience stores that sell gasoline and convenience items in the United States. Each store is individually owned by a YaYa Gas-n-Grub franchisee who pays fees to the franchisor for services. Gasoline is delivered by truck from regional distribution terminals by the current supplier. Each truck delivers 8,000 gallons at a fixed charge of $700 per delivery or $0.0875 per gallon. Franchise owners are eager to reduce delivery costs to boost profits.</p>
+<p>YaYa Gas-n-Grub decides to accept proposals from other distribution terminals, “A” and “B”, to supply the franchise operators. Rather than a fixed fee per delivery, they proposed pricing based on location. But they already have existing customers, “A” and “B” can only provide a limited amount of gasoline to new customers totaling 100,000 and 80,000 gallons respectively. The only difference between the new suppliers and the current supplier is the delivery charge.</p>
 <p>The following chart shows the pricing of gasoline delivery in cents/gallon.</p>
+<div align="center">
 <table class="table">
 <thead>
 <tr class="row-odd"><th class="head text-left"><p>Franchisee <br>  </p></th>
 <th class="head text-right"><p>Demand <br>  </p></th>
+<th class="head text-center"><p>Current Supplier <br> 500,000</p></th>
 <th class="head text-center"><p>Terminal A <br> 100,000</p></th>
 <th class="head text-center"><p>Terminal B <br> 80,000</p></th>
-<th class="head text-center"><p>Current Supplier <br> 500,000</p></th>
 </tr>
 </thead>
 <tbody>
 <tr class="row-even"><td class="text-left"><p>Alice</p></td>
 <td class="text-right"><p>30,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>8.3</p></td>
 <td class="text-center"><p>10.2</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-odd"><td class="text-left"><p>Badri</p></td>
 <td class="text-right"><p>40,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>8.1</p></td>
 <td class="text-center"><p>12.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-even"><td class="text-left"><p>Cara</p></td>
 <td class="text-right"><p>50,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>8.3</p></td>
 <td class="text-center"><p>-</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-odd"><td class="text-left"><p>Dan</p></td>
 <td class="text-right"><p>80,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>9.3</p></td>
 <td class="text-center"><p>8.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-even"><td class="text-left"><p>Emma</p></td>
 <td class="text-right"><p>30,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>10.1</p></td>
 <td class="text-center"><p>10.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-odd"><td class="text-left"><p>Fujita</p></td>
 <td class="text-right"><p>45,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>9.8</p></td>
 <td class="text-center"><p>10.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-even"><td class="text-left"><p>Grace</p></td>
 <td class="text-right"><p>80,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>-</p></td>
 <td class="text-center"><p>8.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-odd"><td class="text-left"><p>Helen</p></td>
 <td class="text-right"><p>18,000</p></td>
+<td class="text-center"><p>8.75</p></td>
 <td class="text-center"><p>7.5</p></td>
 <td class="text-center"><p>10.0</p></td>
-<td class="text-center"><p>8.75</p></td>
 </tr>
 <tr class="row-even"><td class="text-left"><p><strong>TOTAL</strong></p></td>
 <td class="text-right"><p><strong>313,000</strong></p></td>
@@ -641,18 +639,22 @@ <h2>Problem: Distributing gasoline to franchise operators<a class="headerlink" h
 </tr>
 </tbody>
 </table>
-<p>The operator of YaYa Gas-n-Grub has the challenge of allocating the gasoline delivery to minimize the cost to the franchise owners. The following model will present a global objective to minimize the total cost of delivery to all franchise owners.</p>
+</div>
+<p>The operator of YaYa Gas-n-Grub wants to allocate gasoline delivery in such a way that the costs to franchise owners are minimized.</p>
 </section>
 <section id="model-1-minimize-total-delivery-cost">
 <h2>Model 1: Minimize total delivery cost<a class="headerlink" href="#model-1-minimize-total-delivery-cost" title="Permalink to this heading">#</a></h2>
-<p>The decision variables for this example are labeled <span class="math notranslate nohighlight">\(x_{d, s}\)</span> where subscript <span class="math notranslate nohighlight">\(d \in 1, \dots, n_d\)</span> refers to the destination of the delivery and subscript <span class="math notranslate nohighlight">\(s \in 1, \dots, n_s\)</span> to the source. The value of <span class="math notranslate nohighlight">\(x_{d,s}\)</span> is the volume of gasoline shipped to destination <span class="math notranslate nohighlight">\(d\)</span> from source <span class="math notranslate nohighlight">\(s\)</span>.
-Given the cost rate <span class="math notranslate nohighlight">\(r_{d, s}\)</span> for shipping one unit of goods from <span class="math notranslate nohighlight">\(d\)</span> to <span class="math notranslate nohighlight">\(s\)</span>, the objective is to minimize the total cost of transporting gasoline from the sources to the destinations subject to meeting the demand requirements, <span class="math notranslate nohighlight">\(D_d\)</span>, at all destinations, and satisfying the supply constraints, <span class="math notranslate nohighlight">\(S_s\)</span>, at all sources. The full mathematical formulation is:</p>
+<p>The first optimization model aims to minimize the total cost of delivery to all franchise owners.</p>
+<p>We introduce the decision variables <span class="math notranslate nohighlight">\(x_{d, s} \geq 0\)</span>, where subscript <span class="math notranslate nohighlight">\(d \in 1, \dots, n_d\)</span> refers to the destination of the delivery and subscript <span class="math notranslate nohighlight">\(s \in 1, \dots, n_s\)</span> to the source. The value of <span class="math notranslate nohighlight">\(x_{d,s}\)</span> is the volume of gasoline shipped to destination <span class="math notranslate nohighlight">\(d\)</span> from source <span class="math notranslate nohighlight">\(s\)</span>.</p>
+<p>Given the cost rate <span class="math notranslate nohighlight">\(r_{d, s}\)</span> for delivering one unit of gasoline from <span class="math notranslate nohighlight">\(d\)</span> to <span class="math notranslate nohighlight">\(s\)</span>, the objective is to minimize the total cost of transporting gasoline from the sources to the destinations subject to meeting the demand requirements, <span class="math notranslate nohighlight">\(D_d\)</span>, at all destinations, and satisfying the supply constraints, <span class="math notranslate nohighlight">\(S_s\)</span>, at all sources.</p>
+<p>In mathematical terms, we can write the full problem as</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
     \min \quad &amp; \sum_{d=1}^{n_d} \sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\
-    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d\in 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
-    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s\in 1, \dots, n_s &amp; \quad \text{(supply constraints)}
+    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d = 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
+    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s = 1, \dots, n_s &amp; \quad \text{(supply constraints)}\\
+    &amp; x_{d, s} \geq 0 &amp; \forall \, d = 1, \dots, n_d, \, s = 1, \dots, n_s.
 \end{align*}
 \end{split}\]</div>
 </section>
@@ -704,7 +706,7 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
 <span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span><span class="s2">&quot;&lt;br&gt;&lt;b&gt;Gasoline Demand (Gallons)&lt;/b&gt;&quot;</span><span class="p">))</span>
 <span class="n">display</span><span class="p">(</span><span class="n">demand</span><span class="o">.</span><span class="n">to_frame</span><span class="p">())</span>
 
-<span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span><span class="s2">&quot;&lt;br&gt;&lt;b&gt;Transportation Rates (€ cents per Gallon)&lt;/b&gt;&quot;</span><span class="p">))</span>
+<span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span><span class="s2">&quot;&lt;br&gt;&lt;b&gt;Transportation Rates ($ cents per Gallon)&lt;/b&gt;&quot;</span><span class="p">))</span>
 <span class="n">display</span><span class="p">(</span><span class="n">rates</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -802,7 +804,7 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
     </tr>
   </tbody>
 </table>
-</div></div><div class="output text_html"><br><b>Transportation Rates (€ cents per Gallon)</b></div><div class="output text_html"><div>
+</div></div><div class="output text_html"><br><b>Transportation Rates ($ cents per Gallon)</b></div><div class="output text_html"><div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
@@ -891,7 +893,7 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
 
 
 <span class="k">def</span> <span class="nf">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span><span class="p">):</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Gasoline distribution&quot;</span><span class="p">)</span>
 
     <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">rates</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">rates</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
@@ -937,7 +939,7 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
 <span class="n">m</span> <span class="o">=</span> <span class="n">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
 
 <span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="p">{</span><span class="n">src</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span> <span class="n">src</span><span class="p">]()</span> <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">}</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
+    <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="p">{</span><span class="n">src</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span> <span class="n">src</span><span class="p">]())</span> <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">}</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
 <span class="p">)</span><span class="o">.</span><span class="n">T</span>
 <span class="n">results</span><span class="p">[</span><span class="s2">&quot;current costs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">700</span> <span class="o">*</span> <span class="n">demand</span> <span class="o">/</span> <span class="mi">8000</span>
 <span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
@@ -946,13 +948,13 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
 <span class="n">results</span><span class="p">[</span><span class="s2">&quot;savings&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;current costs&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">results</span><span class="p">[</span>
     <span class="s2">&quot;contract costs&quot;</span>
 <span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
     <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
 <span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old Delivery Costs = </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">€&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New Delivery Costs = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">€</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old delivery costs = $</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New delivery costs = $</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
 
 <span class="n">results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;savings&quot;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">)</span>
@@ -961,8 +963,8 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old Delivery Costs = 27387.50€
-New Delivery Costs = 26113.50€
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old delivery costs = $27387.50
+New delivery costs = $26113.50
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -996,108 +998,110 @@ <h2>Data Entry<a class="headerlink" href="#data-entry" title="Permalink to this
   <tbody>
     <tr>
       <th>Alice</th>
-      <td>30000.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
+      <td>30000</td>
+      <td>0</td>
+      <td>0</td>
       <td>2625.0</td>
       <td>2490.0</td>
       <td>135.0</td>
-      <td>0.0830</td>
-      <td>0.0875</td>
+      <td>8.30</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Badri</th>
-      <td>40000.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
+      <td>40000</td>
+      <td>0</td>
+      <td>0</td>
       <td>3500.0</td>
       <td>3240.0</td>
       <td>260.0</td>
-      <td>0.0810</td>
-      <td>0.0855</td>
+      <td>8.10</td>
+      <td>8.55</td>
     </tr>
     <tr>
       <th>Cara</th>
-      <td>12000.0</td>
-      <td>0.0</td>
-      <td>38000.0</td>
+      <td>12000</td>
+      <td>0</td>
+      <td>38000</td>
       <td>4375.0</td>
       <td>4321.0</td>
       <td>54.0</td>
-      <td>0.0864</td>
-      <td>0.0875</td>
+      <td>8.64</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Dan</th>
-      <td>0.0</td>
-      <td>20000.0</td>
-      <td>0.0</td>
+      <td>0</td>
+      <td>20000</td>
+      <td>0</td>
       <td>1750.0</td>
       <td>1600.0</td>
       <td>150.0</td>
-      <td>0.0800</td>
-      <td>0.0875</td>
+      <td>8.00</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Emma</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>30000.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>30000</td>
       <td>2625.0</td>
       <td>2625.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.0875</td>
+      <td>8.75</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Fujita</th>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>45000.0</td>
+      <td>0</td>
+      <td>0</td>
+      <td>45000</td>
       <td>3937.5</td>
       <td>3937.5</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.0875</td>
+      <td>8.75</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Grace</th>
-      <td>0.0</td>
-      <td>60000.0</td>
-      <td>20000.0</td>
+      <td>0</td>
+      <td>60000</td>
+      <td>20000</td>
       <td>7000.0</td>
       <td>6550.0</td>
       <td>450.0</td>
-      <td>0.0819</td>
-      <td>0.0875</td>
+      <td>8.19</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Helen</th>
-      <td>18000.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
+      <td>18000</td>
+      <td>0</td>
+      <td>0</td>
       <td>1575.0</td>
       <td>1350.0</td>
       <td>225.0</td>
-      <td>0.0750</td>
-      <td>0.0795</td>
+      <td>7.50</td>
+      <td>7.95</td>
     </tr>
   </tbody>
 </table>
-</div></div><img alt="../../_images/df69d323662a3f300a3b2b6a1bad82dc8b71938ab78f89ec600d3d225109f8de.png" src="../../_images/df69d323662a3f300a3b2b6a1bad82dc8b71938ab78f89ec600d3d225109f8de.png" />
+</div></div><img alt="../../_images/98ab93bd8361c7d84f3180808803d4b21d0cca066c8db29f83ae3012feadff2c.png" src="../../_images/98ab93bd8361c7d84f3180808803d4b21d0cca066c8db29f83ae3012feadff2c.png" />
 </div>
 </div>
 </section>
 <section id="model-2-minimize-cost-rate-for-franchise-owners">
 <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="#model-2-minimize-cost-rate-for-franchise-owners" title="Permalink to this heading">#</a></h2>
-<p>Minimizing total costs provides no guarantee that individual franchise owners will benefit equally, or in fact benefit at all, from minimizing total costs. In this example neither Emma or Fujita would save any money on delivery costs, and the majority of savings goes to just two of the franchisees.  Without a better distribution of the benefits there may be little enthusiasm among the franchisees to adopt change. This observation motivates an attempt at a second model. In this case the objective is minimize a common rate for the cost of gasoline distribution subject to meeting the demand and supply constraints, <span class="math notranslate nohighlight">\(S_s\)</span>, at all sources. The mathematical formulation of this different problem is as follows:</p>
+<p>Minimizing total costs provides no guarantee that individual franchise owners will benefit equally, or in fact benefit at all, from minimizing total costs. In this example neither Emma or Fujita would save any money on delivery costs, and the majority of savings goes to just one of the franchisees. Without a better distribution of the benefits there may be little enthusiasm among the franchisees to adopt change. This observation motivates an attempt at a second model. In this case the objective is minimizing a common rate for the cost of gasoline distribution subject to meeting the demand and supply constraints, <span class="math notranslate nohighlight">\(S_s\)</span>, at all sources.</p>
+<p>The mathematical formulation of this different problem is as follows:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
     \min \quad &amp; \rho \\
-    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d\in 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
-    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s\in 1, \dots, n_s &amp; \quad \text{(supply constraints)}\\
-    &amp; \sum_{s=1}^{n_s} r_{d, s} x_{d, s} \leq \rho D_d &amp; \forall d\in 1, \dots, n_d &amp; \quad \text{(common cost rate)}\\
+    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d = 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
+    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s =  1, \dots, n_s &amp; \quad \text{(supply constraints)}\\
+    &amp; \sum_{s=1}^{n_s} r_{d, s} x_{d, s} \leq \rho D_d &amp; \forall d = 1, \dots, n_d &amp; \quad \text{(common cost rate)}\\
+    &amp; x_{d, s} \geq 0 &amp; \forall \, d = 1, \dots, n_d, \, s = 1, \dots, n_s.
 \end{align*}
 \end{split}\]</div>
 <p>The following Pyomo model implements this formulation.</p>
@@ -1106,7 +1110,7 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
 
 
-<span class="k">def</span> <span class="nf">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">transport_v2</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span><span class="p">):</span>
     <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
 
     <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">rates</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
@@ -1157,7 +1161,7 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
     <span class="k">return</span> <span class="n">m</span>
 
 
-<span class="n">m</span> <span class="o">=</span> <span class="n">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">transport_v2</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
 
 <span class="n">results</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span>
     <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
@@ -1172,13 +1176,13 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
 <span class="n">results</span><span class="p">[</span><span class="s2">&quot;savings&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;current costs&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">results</span><span class="p">[</span>
     <span class="s2">&quot;contract costs&quot;</span>
 <span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-    <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+    <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]],</span> <span class="mi">4</span><span class="p">)</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
 <span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old Delivery Costs = $ </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New Delivery Costs = $ </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old delivery costs = $</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New delivery costs = $</span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">(),</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
 
 <span class="n">results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;savings&quot;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">)</span>
@@ -1187,8 +1191,8 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old Delivery Costs = $ 27387.5
-New Delivery Costs = $ 27387.49999
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old delivery costs = $27387.5
+New delivery costs = $27387.5
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -1228,8 +1232,8 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
       <td>2625.0</td>
       <td>2625.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Badri</th>
@@ -1239,19 +1243,19 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
       <td>3500.0</td>
       <td>3500.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Cara</th>
+      <td>49754.6</td>
+      <td>245.4</td>
       <td>0.0</td>
-      <td>0.0</td>
-      <td>50000.0</td>
       <td>4375.0</td>
       <td>4375.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000002</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Dan</th>
@@ -1261,8 +1265,8 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
       <td>1750.0</td>
       <td>1750.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Emma</th>
@@ -1272,8 +1276,8 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
       <td>2625.0</td>
       <td>2625.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Fujita</th>
@@ -1283,50 +1287,51 @@ <h2>Model 2: Minimize cost rate for franchise owners<a class="headerlink" href="
       <td>3937.5</td>
       <td>3937.5</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Grace</th>
-      <td>652.2</td>
-      <td>79347.8</td>
       <td>0.0</td>
+      <td>0.0</td>
+      <td>80000.0</td>
       <td>7000.0</td>
       <td>7000.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
     <tr>
       <th>Helen</th>
-      <td>-0.0</td>
+      <td>0.0</td>
       <td>0.0</td>
       <td>18000.0</td>
       <td>1575.0</td>
       <td>1575.0</td>
       <td>0.0</td>
-      <td>0.0875</td>
-      <td>0.000000</td>
+      <td>8.75</td>
+      <td>0.0</td>
     </tr>
   </tbody>
 </table>
-</div></div><img alt="../../_images/92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png" src="../../_images/92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png" />
+</div></div><img alt="../../_images/50ec4a19840420dc3d3aecb825aa3214cbfa7081877adee3b8ad89bf91985bca.png" src="../../_images/50ec4a19840420dc3d3aecb825aa3214cbfa7081877adee3b8ad89bf91985bca.png" />
 </div>
 </div>
 </section>
 <section id="model-3-minimize-total-cost-for-a-cost-sharing-plan">
 <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" href="#model-3-minimize-total-cost-for-a-cost-sharing-plan" title="Permalink to this heading">#</a></h2>
-<p>The prior two models demonstrated some practical difficulties in realizing the benefits of a cost optimization plan. Model 1 will likely fail in a franchiser/franchisee arrangement because the realized savings would be fore the benefit of a few.</p>
-<p>Model 2 was an attempt to remedy the problem by solving for an allocation of deliveries that would lower the cost rate that would be paid by each franchisee directly to the gasoline distributors. Perhaps surprisingly, the resulting solution offered no savings to any franchisee. Inspecting the data shows the source of the problem is that two franchisees, Emma and Fujita, simply have no lower cost alternative than the current supplier. Therefore finding a distribution plan with direct payments to the distributors that lowers everyone’s cost is an impossible task.</p>
-<p>The third model addresses this problem with a plan to share the cost savings among the franchisees. In this plan, the franchiser would collect delivery fees from the franchisees to pay the gasoline distributors. The optimization objective returns to the problem to minimizing total delivery costs, but then adds a constraint that defines a common cost rate to charge all franchisees. By offering a benefit to all parties, the franchiser offers incentive for group participation in contracting for gasoline distribution services.</p>
+<p>The prior two models demonstrated some practical difficulties in realizing the benefits of a cost optimization plan. Model 1 will likely fail in a franchiser/franchisee arrangement because the realized savings would be for the benefit of a few.</p>
+<p>Model 2 was an attempt to remedy the problem by solving for an allocation of deliveries that would lower the cost rate that would be paid by each franchisee directly to the gasoline distributors. Perhaps surprisingly, the resulting solution offered no savings to any franchisee. Inspecting the data shows the source of the problem is that two franchisees, Emma and Fujita, simply have no lower cost alternative than the current supplier. Therefore, finding a distribution plan with direct payments to the distributors that lowers everyone’s cost is an impossible task.</p>
+<p>We now consider a third model that addresses this problem with a plan to share the cost savings among the franchisees. In this plan, the franchiser would collect delivery fees from the franchisees to pay the gasoline distributors. The optimization objective returns to the problem to minimizing total delivery costs, but then adds a constraint that defines a common cost rate to charge all franchisees. By offering a benefit to all parties, the franchiser offers incentive for group participation in contracting for gasoline distribution services.</p>
 <p>In mathematical terms, the problem can be formulated as follows:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
     \min \quad &amp; \sum_{d=1}^{n_d} \sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\
-    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d\in 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
-    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s\in 1, \dots, n_s &amp; \quad \text{(supply constraints)}\\
-    &amp; \sum_{s=1}^{n_s} r_{d, s} x_{d, s} = \rho D_d &amp; \forall d\in 1, \dots, n_d &amp; \quad \text{(uniform cost sharing rate)}\\
+    \text{s.t.} \quad &amp;\sum_{s=1}^{n_s} x_{d, s}  = D_d &amp; \forall \, d= 1, \dots, n_d &amp; \quad \text{(demand constraints)}\\
+    &amp; \sum_{d=1}^{n_d} x_{d, s}  \leq S_s &amp; \forall \, s = 1, \dots, n_s &amp; \quad \text{(supply constraints)}\\
+    &amp; \sum_{s=1}^{n_s} r_{d, s} x_{d, s} = \rho D_d &amp; \forall d= 1, \dots, n_d &amp; \quad \text{(uniform cost sharing rate)}\\
+    &amp; x_{d, s} \geq 0 &amp; \forall \, d = 1, \dots, n_d, \, s = 1, \dots, n_s.
 \end{align*}
 \end{split}\]</div>
 <div class="cell docutils container">
@@ -1334,7 +1339,7 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
 
 
-<span class="k">def</span> <span class="nf">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">transport_v3</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span><span class="p">):</span>
     <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
 
     <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">rates</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
@@ -1383,7 +1388,7 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
     <span class="k">return</span> <span class="n">m</span>
 
 
-<span class="n">m</span> <span class="o">=</span> <span class="n">transport</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">transport_v3</span><span class="p">(</span><span class="n">supply</span><span class="p">,</span> <span class="n">demand</span><span class="p">,</span> <span class="n">rates</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span>
 
 <span class="n">results</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span>
     <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
@@ -1398,13 +1403,13 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
 <span class="n">results</span><span class="p">[</span><span class="s2">&quot;savings&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;current costs&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">results</span><span class="p">[</span>
     <span class="s2">&quot;contract costs&quot;</span>
 <span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="nb">round</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;contract costs&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">demand</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">results</span><span class="p">[</span><span class="s2">&quot;marginal cost&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
     <span class="p">{</span><span class="n">dst</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span> <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">}</span>
 <span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old Delivery Costs = </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">€&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New Delivery Costs = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">€</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Old delivery costs = $</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">demand</span><span class="p">)</span><span class="o">*</span><span class="mi">700</span><span class="o">/</span><span class="mi">8000</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New delivery costs = $</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">total_cost</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
 
 <span class="n">results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;savings&quot;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">)</span>
@@ -1413,8 +1418,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old Delivery Costs = 27387.50€
-New Delivery Costs = 26113.50€
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old delivery costs = $27387.50
+New delivery costs = $26113.50
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -1454,8 +1459,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>2625.0</td>
       <td>2502.9</td>
       <td>122.1</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Badri</th>
@@ -1465,8 +1470,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>3500.0</td>
       <td>3337.2</td>
       <td>162.8</td>
-      <td>0.0834</td>
-      <td>0.0855</td>
+      <td>8.34</td>
+      <td>8.55</td>
     </tr>
     <tr>
       <th>Cara</th>
@@ -1476,8 +1481,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>4375.0</td>
       <td>4171.5</td>
       <td>203.5</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Dan</th>
@@ -1487,8 +1492,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>1750.0</td>
       <td>1668.6</td>
       <td>81.4</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Emma</th>
@@ -1498,8 +1503,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>2625.0</td>
       <td>2502.9</td>
       <td>122.1</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Fujita</th>
@@ -1509,8 +1514,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>3937.5</td>
       <td>3754.3</td>
       <td>183.2</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Grace</th>
@@ -1520,8 +1525,8 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>7000.0</td>
       <td>6674.4</td>
       <td>325.6</td>
-      <td>0.0834</td>
-      <td>0.0875</td>
+      <td>8.34</td>
+      <td>8.75</td>
     </tr>
     <tr>
       <th>Helen</th>
@@ -1531,12 +1536,12 @@ <h2>Model 3: Minimize total cost for a cost-sharing plan<a class="headerlink" hr
       <td>1575.0</td>
       <td>1501.7</td>
       <td>73.3</td>
-      <td>0.0834</td>
-      <td>0.0795</td>
+      <td>8.34</td>
+      <td>7.95</td>
     </tr>
   </tbody>
 </table>
-</div></div><img alt="../../_images/b7cf18e448ef42aa288998b9fc6fb8513e528772d8cb4c281456ce4a9d944af1.png" src="../../_images/b7cf18e448ef42aa288998b9fc6fb8513e528772d8cb4c281456ce4a9d944af1.png" />
+</div></div><img alt="../../_images/f859de7d78ebccfd6dd6b4f485fae86ce571d705bbaf85dfdeb4f2c3e47c85eb.png" src="../../_images/f859de7d78ebccfd6dd6b4f485fae86ce571d705bbaf85dfdeb4f2c3e47c85eb.png" />
 </div>
 </div>
 </section>
@@ -1556,6 +1561,7 @@ <h2>Comparing model results<a class="headerlink" href="#comparing-model-results"
 <span class="n">model3_results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;savings&quot;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
 <span class="n">model3_results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;savings&quot;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Model 1&quot;</span><span class="p">,</span> <span class="s2">&quot;Model 3&quot;</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">470</span><span class="p">)</span>
 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Delivery costs by franchise&quot;</span><span class="p">)</span>
 
 <span class="n">model1_results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;contract rate&quot;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
@@ -1563,7 +1569,7 @@ <h2>Comparing model results<a class="headerlink" href="#comparing-model-results"
 
 <span class="n">model3_results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;contract rate&quot;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
 <span class="n">model3_results</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;contract rate&quot;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.09</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">7.25</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Model 1&quot;</span><span class="p">,</span> <span class="s2">&quot;Model 3&quot;</span><span class="p">])</span>
 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Contract rates by franchise&quot;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
@@ -1571,7 +1577,7 @@ <h2>Comparing model results<a class="headerlink" href="#comparing-model-results"
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png" src="../../_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png" />
+<img alt="../../_images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png" src="../../_images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png" />
 </div>
 </div>
 </section>
@@ -1772,20 +1778,20 @@ <h3>Manually formatted reports<a class="headerlink" href="#manually-formatted-re
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Constraint: supply_constraint&quot;</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]]</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.0f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]]</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">&quot;</span>
     <span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Constraint: demand_constraint&quot;</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dst</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dst</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.0f</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">&quot;</span>
     <span class="p">)</span>
 
 <span class="c1"># Decision variable reports</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Decision variables: x&quot;</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
     <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2"> -&gt; </span><span class="si">{</span><span class="n">dst</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2"> -&gt; </span><span class="si">{</span><span class="n">dst</span><span class="si">:</span><span class="s2">12s</span><span class="si">}</span><span class="s2">  </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]()</span><span class="si">:</span><span class="s2">8.0f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -1795,47 +1801,47 @@ <h3>Manually formatted reports<a class="headerlink" href="#manually-formatted-re
 cost = 26113.5
 
 Constraint: supply_constraint
-Terminal A    100000.00     -0.00
-Terminal B    80000.00     -0.01
-Current Supplier  133000.00      0.00
+Terminal A      100000     -0.00
+Terminal B       80000     -0.01
+Current Supplier    133000      0.00
 
 Constraint: demand_constraint
-Alice         30000.00      0.09
-Badri         40000.00      0.09
-Cara          50000.00      0.09
-Dan           20000.00      0.09
-Emma          30000.00      0.09
-Fujita        45000.00      0.09
-Grace         80000.00      0.09
-Helen         18000.00      0.08
+Alice            30000      0.09
+Badri            40000      0.09
+Cara             50000      0.09
+Dan              20000      0.09
+Emma             30000      0.09
+Fujita           45000      0.09
+Grace            80000      0.09
+Helen            18000      0.08
 
 Decision variables: x
-Terminal A   -&gt; Alice         30000.00
-Terminal A   -&gt; Badri         40000.00
-Terminal A   -&gt; Cara          12000.00
-Terminal A   -&gt; Dan               0.00
-Terminal A   -&gt; Emma              0.00
-Terminal A   -&gt; Fujita            0.00
-Terminal A   -&gt; Grace             0.00
-Terminal A   -&gt; Helen         18000.00
-
-Terminal B   -&gt; Alice             0.00
-Terminal B   -&gt; Badri             0.00
-Terminal B   -&gt; Cara              0.00
-Terminal B   -&gt; Dan           20000.00
-Terminal B   -&gt; Emma              0.00
-Terminal B   -&gt; Fujita            0.00
-Terminal B   -&gt; Grace         60000.00
-Terminal B   -&gt; Helen             0.00
-
-Current Supplier -&gt; Alice             0.00
-Current Supplier -&gt; Badri             0.00
-Current Supplier -&gt; Cara          38000.00
-Current Supplier -&gt; Dan               0.00
-Current Supplier -&gt; Emma          30000.00
-Current Supplier -&gt; Fujita        45000.00
-Current Supplier -&gt; Grace         20000.00
-Current Supplier -&gt; Helen             0.00
+Terminal A   -&gt; Alice            30000
+Terminal A   -&gt; Badri            40000
+Terminal A   -&gt; Cara             12000
+Terminal A   -&gt; Dan                  0
+Terminal A   -&gt; Emma                 0
+Terminal A   -&gt; Fujita               0
+Terminal A   -&gt; Grace                0
+Terminal A   -&gt; Helen            18000
+
+Terminal B   -&gt; Alice                0
+Terminal B   -&gt; Badri                0
+Terminal B   -&gt; Cara                 0
+Terminal B   -&gt; Dan              20000
+Terminal B   -&gt; Emma                 0
+Terminal B   -&gt; Fujita               0
+Terminal B   -&gt; Grace            60000
+Terminal B   -&gt; Helen                0
+
+Current Supplier -&gt; Alice                0
+Current Supplier -&gt; Badri                0
+Current Supplier -&gt; Cara             38000
+Current Supplier -&gt; Dan                  0
+Current Supplier -&gt; Emma             30000
+Current Supplier -&gt; Fujita           45000
+Current Supplier -&gt; Grace            20000
+Current Supplier -&gt; Helen                0
 </pre></div>
 </div>
 </div>
@@ -1877,6 +1883,7 @@ <h3>Pandas<a class="headerlink" href="#pandas" title="Permalink to this heading"
 <span class="p">)</span><span class="o">.</span><span class="n">T</span>
 <span class="n">display</span><span class="p">(</span><span class="n">shipments</span><span class="p">)</span>
 <span class="n">shipments</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -2074,58 +2081,55 @@ <h3>Pandas<a class="headerlink" href="#pandas" title="Permalink to this heading"
     </tr>
   </tbody>
 </table>
-</div></div><img alt="../../_images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png" src="../../_images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png" />
+</div></div><img alt="../../_images/af2d6ffb05a0789e8f8c5abe8f69f66c2c62f3b23009b4e8b66c8d16b7313ae3.png" src="../../_images/af2d6ffb05a0789e8f8c5abe8f69f66c2c62f3b23009b4e8b66c8d16b7313ae3.png" />
 </div>
 </div>
 </section>
 <section id="graphviz">
 <h3>Graphviz<a class="headerlink" href="#graphviz" title="Permalink to this heading">#</a></h3>
-<p>The <code class="docutils literal notranslate"><span class="pre">graphviz</span></code> utility is a collection of tools for visually graphs and directed graphs. Unfortunately, the package can be troublesome to install on laptops in a way that is compatible with many JupyterLab installations. Accordingly, the following cell is intended for use on Google Colab which provides a preinstalled version of <code class="docutils literal notranslate"><span class="pre">graphviz</span></code>.</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">graphviz</span></code> utility is a collection of tools for visually graphs and directed graphs.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">graphviz</span> <span class="kn">import</span> <span class="n">Digraph</span>
 
+<span class="n">dot</span> <span class="o">=</span> <span class="n">Digraph</span><span class="p">(</span>
+    <span class="n">node_attr</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fontsize&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">,</span> <span class="s2">&quot;shape&quot;</span><span class="p">:</span> <span class="s2">&quot;rectangle&quot;</span><span class="p">,</span> <span class="s2">&quot;style&quot;</span><span class="p">:</span> <span class="s2">&quot;filled&quot;</span><span class="p">},</span>
+    <span class="n">edge_attr</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fontsize&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">},</span>
+<span class="p">)</span>
 
-<span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">graphviz</span>
-    <span class="kn">from</span> <span class="nn">graphviz</span> <span class="kn">import</span> <span class="n">Digraph</span>
-
-    <span class="n">dot</span> <span class="o">=</span> <span class="n">Digraph</span><span class="p">(</span>
-        <span class="n">node_attr</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fontsize&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">,</span> <span class="s2">&quot;shape&quot;</span><span class="p">:</span> <span class="s2">&quot;rectangle&quot;</span><span class="p">,</span> <span class="s2">&quot;style&quot;</span><span class="p">:</span> <span class="s2">&quot;filled&quot;</span><span class="p">},</span>
-        <span class="n">edge_attr</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fontsize&quot;</span><span class="p">:</span> <span class="s2">&quot;10&quot;</span><span class="p">},</span>
+<span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">supply = </span><span class="si">{</span><span class="n">supply</span><span class="p">[</span><span class="n">src</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sens  = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span>
     <span class="p">)</span>
+    <span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">fillcolor</span><span class="o">=</span><span class="s2">&quot;lightblue&quot;</span><span class="p">)</span>
 
-    <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
-        <span class="n">label</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">src</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">supply = </span><span class="si">{</span><span class="n">supply</span><span class="p">[</span><span class="n">src</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sens  = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">supply_constraint</span><span class="p">[</span><span class="n">src</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="p">)</span>
-        <span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">fillcolor</span><span class="o">=</span><span class="s2">&quot;lightblue&quot;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">:</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dst</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">demand = </span><span class="si">{</span><span class="n">demand</span><span class="p">[</span><span class="n">dst</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sens  = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+    <span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">dst</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">fillcolor</span><span class="o">=</span><span class="s2">&quot;gold&quot;</span><span class="p">)</span>
 
+<span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
     <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">:</span>
-        <span class="n">label</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dst</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">demand = </span><span class="si">{</span><span class="n">demand</span><span class="p">[</span><span class="n">dst</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span>
-            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">sens  = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">dual</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">demand_constraint</span><span class="p">[</span><span class="n">dst</span><span class="p">]]</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="p">)</span>
-        <span class="n">dot</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">dst</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">fillcolor</span><span class="o">=</span><span class="s2">&quot;gold&quot;</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">src</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">SOURCES</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">dst</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">DESTINATIONS</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span> <span class="n">src</span><span class="p">]()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span>
-                    <span class="n">src</span><span class="p">,</span>
-                    <span class="n">dst</span><span class="p">,</span>
-                    <span class="sa">f</span><span class="s2">&quot;rate = </span><span class="si">{</span><span class="n">rates</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]</span><span class="si">}</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-                <span class="p">)</span>
-
-    <span class="n">display</span><span class="p">(</span><span class="n">dot</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span> <span class="n">src</span><span class="p">]()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">dot</span><span class="o">.</span><span class="n">edge</span><span class="p">(</span>
+                <span class="n">src</span><span class="p">,</span>
+                <span class="n">dst</span><span class="p">,</span>
+                <span class="sa">f</span><span class="s2">&quot;rate = </span><span class="si">{</span><span class="n">rates</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]</span><span class="si">}</span><span class="se">\n</span><span class="s2">shipped = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">dst</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">]()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+
+<span class="n">display</span><span class="p">(</span><span class="n">dot</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
+<div class="cell_output docutils container">
+<img alt="../../_images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg" src="../../_images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg" /></div>
 </div>
 </section>
 </section>
diff --git a/searchindex.js b/searchindex.js
index daffc9c0..ac111eb8 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["genindex", "intro", "notebooks/01/01.00", "notebooks/01/production-planning", "notebooks/01/production-planning-advanced", "notebooks/01/production-planning-basic", "notebooks/02/02.00", "notebooks/02/L1-regression-wine-quality", "notebooks/02/bim", "notebooks/02/bim-dual", "notebooks/02/bim-fractional", "notebooks/02/bim-maxmin", "notebooks/02/bim-rawmaterialplanning", "notebooks/02/lad-regression", "notebooks/02/mad-portfolio-optimization", "notebooks/02/multiproductionfaciliity_worstcase", "notebooks/03/03.00", "notebooks/03/bim-perturbed", "notebooks/03/bim-production-revisited", "notebooks/03/cryptarithms", "notebooks/03/facility-location", "notebooks/03/job-shop-scheduling", "notebooks/03/machine-scheduling", "notebooks/03/maintenance-planning", "notebooks/03/recharging-electric-vehicle", "notebooks/03/shift-scheduling", "notebooks/03/simple-production-model-gdp", "notebooks/03/strip-packing", "notebooks/04/04.00", "notebooks/04/cryptocurrency-arbitrage", "notebooks/04/dinner-seat-allocation", "notebooks/04/forex-arbitrage", "notebooks/04/gasoline-distribution", "notebooks/04/mincost-flow", "notebooks/04/power-network", "notebooks/05/05.00", "notebooks/05/cutting-stock", "notebooks/05/markowitz_portfolio", "notebooks/05/milk-pooling", "notebooks/05/ols-regression", "notebooks/05/refinery-production", "notebooks/05/svm", "notebooks/06/06.00", "notebooks/06/building-insulation", "notebooks/06/economic-order-quantity", "notebooks/06/investment-wheel", "notebooks/06/kelly-criterion", "notebooks/06/markowitz_portfolio_revisited", "notebooks/06/optimal-growth-portfolios", "notebooks/06/svm-conic", "notebooks/07/07.00", "notebooks/07/bim-robustness-analysis", "notebooks/07/fleet-assignment", "notebooks/08/08.00", "notebooks/08/bim-robust-optimization", "notebooks/09/09.00", "notebooks/09/economicdispatch", "notebooks/09/markowitz_portfolio_with_chance_constraint", "notebooks/09/pop-up_shop", "notebooks/09/seafood", "notebooks/10/10.00", "notebooks/10/airline-seating", "notebooks/10/ccg", "notebooks/10/farmer", "notebooks/10/opf-ldr", "notebooks/10/opf-wind-curtailment", "notebooks/appendix/appendix", "notebooks/appendix/functional-programming-pyomo", "notebooks/appendix/pyomo-style-guide"], "filenames": ["genindex.md", "intro.md", "notebooks/01/01.00.md", "notebooks/01/production-planning.ipynb", "notebooks/01/production-planning-advanced.ipynb", "notebooks/01/production-planning-basic.ipynb", "notebooks/02/02.00.md", "notebooks/02/L1-regression-wine-quality.ipynb", "notebooks/02/bim.ipynb", "notebooks/02/bim-dual.ipynb", "notebooks/02/bim-fractional.ipynb", "notebooks/02/bim-maxmin.ipynb", "notebooks/02/bim-rawmaterialplanning.ipynb", "notebooks/02/lad-regression.ipynb", "notebooks/02/mad-portfolio-optimization.ipynb", "notebooks/02/multiproductionfaciliity_worstcase.ipynb", "notebooks/03/03.00.md", "notebooks/03/bim-perturbed.ipynb", "notebooks/03/bim-production-revisited.ipynb", "notebooks/03/cryptarithms.ipynb", "notebooks/03/facility-location.ipynb", "notebooks/03/job-shop-scheduling.ipynb", "notebooks/03/machine-scheduling.ipynb", "notebooks/03/maintenance-planning.ipynb", "notebooks/03/recharging-electric-vehicle.ipynb", "notebooks/03/shift-scheduling.ipynb", "notebooks/03/simple-production-model-gdp.ipynb", "notebooks/03/strip-packing.ipynb", "notebooks/04/04.00.md", "notebooks/04/cryptocurrency-arbitrage.ipynb", "notebooks/04/dinner-seat-allocation.ipynb", "notebooks/04/forex-arbitrage.ipynb", "notebooks/04/gasoline-distribution.ipynb", "notebooks/04/mincost-flow.ipynb", "notebooks/04/power-network.ipynb", "notebooks/05/05.00.md", "notebooks/05/cutting-stock.ipynb", "notebooks/05/markowitz_portfolio.ipynb", "notebooks/05/milk-pooling.ipynb", "notebooks/05/ols-regression.ipynb", "notebooks/05/refinery-production.ipynb", "notebooks/05/svm.ipynb", "notebooks/06/06.00.md", "notebooks/06/building-insulation.ipynb", "notebooks/06/economic-order-quantity.ipynb", "notebooks/06/investment-wheel.ipynb", "notebooks/06/kelly-criterion.ipynb", "notebooks/06/markowitz_portfolio_revisited.ipynb", "notebooks/06/optimal-growth-portfolios.ipynb", "notebooks/06/svm-conic.ipynb", "notebooks/07/07.00.md", "notebooks/07/bim-robustness-analysis.ipynb", "notebooks/07/fleet-assignment.ipynb", "notebooks/08/08.00.md", "notebooks/08/bim-robust-optimization.ipynb", "notebooks/09/09.00.md", "notebooks/09/economicdispatch.ipynb", "notebooks/09/markowitz_portfolio_with_chance_constraint.ipynb", "notebooks/09/pop-up_shop.ipynb", "notebooks/09/seafood.ipynb", "notebooks/10/10.00.md", "notebooks/10/airline-seating.ipynb", "notebooks/10/ccg.ipynb", "notebooks/10/farmer.ipynb", "notebooks/10/opf-ldr.ipynb", "notebooks/10/opf-wind-curtailment.ipynb", "notebooks/appendix/appendix.md", "notebooks/appendix/functional-programming-pyomo.ipynb", "notebooks/appendix/pyomo-style-guide.md"], "titles": ["Index", "Hands-On Optimization with Python", "1. Mathematical Optimization", "A first production planning problem", "A data-driven Pyomo Model", "A basic Pyomo model", "2. Linear Optimization", "Extra material: Wine quality prediction with <span class=\"math notranslate nohighlight\">\\(L_1\\)</span> regression", "BIM production", "Dual of the BIM production problem", "BIM production variants", "BIM production for worst case", "BIM production using demand forecasts", "Least Absolute Deviation (LAD) Regression", "Mean Absolute Deviation (MAD) portfolio optimization", "Extra material: Multi-product facility production", "3. Mixed Integer Linear Optimization", "BIM production with perturbed data", "BIM production revisited", "Extra material: Cryptarithms puzzle", "Facility location problem", "Extra material: Job shop scheduling", "Machine Scheduling", "Extra material: Maintenance planning", "Recharging strategy for an electric vehicle", "Workforce shift scheduling", "Production model using disjunctions", "Extra material: Strip packing", "4. Network Optimization", "Cryptocurrency arbitrage search", "Dinner seating arrangement", "Forex Arbitrage", "Gasoline distribution", "Minimum-Cost Flow Problem", "Extra material: Energy dispatch problem", "5. Convex Optimization", "Extra Material: Cutting Stock", "Markowitz portfolio optimization", "Milk pooling and blending", "Ordinary Least Squares (OLS) Regression", "Extra material: Refinery production and shadow pricing with CVXPY", "Support Vector Machines for Binary Classification", "6. Conic Optimization", "Optimal Design of Multilayered Building Insulation", "Economic Order Quantity", "Extra material: Luenberger\u2019s Investment Wheel", "The Kelly Criterion", "Markowitz portfolio optimization revisited", "Extra material: Optimal Growth Portfolios with Risk Aversion", "Training Support Vector Machines with Conic Optimization", "7. Accounting for Uncertainty: Optimization Meets Reality", "Robustness analysis of BIM production plan via simulations", "Fleet assignment problem", "8. Robust Optimization - Single Stage Problems", "Robust BIM microchip production problem", "9. Stochastic Optimization - Single Stage Problems", "Economic dispatch in energy systems", "Markowitz portfolio optimization with chance constraints", "Pop-up shop", "Stock optimization for seafood distribution center", "10. Two-Stage Problems", "Airline seat allocation problem", "Two-stage Production Planning", "Extra: The farmer\u2019s problem and its variants", "Optimal power flow problem with recourse actions", "Extra: Two-stage energy dispatch optimization with wind curtailment", "Appendix: Working with Pyomo", "Functional Programming with Pyomo", "Pyomo style guide"], "terms": {"welcom": [1, 5, 68], "thi": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68], "repositori": [1, 7, 25, 54, 56], "companion": [1, 5, 6, 16, 28, 35, 42, 53, 55, 60], "notebook": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48, 49, 51, 54, 56, 59, 62, 64, 65, 66, 67], "book": [1, 8, 15, 20, 22, 34, 41, 45, 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43, 56, 67, 68], "whether": [2, 8, 22, 24, 26, 27, 29, 31, 34, 35, 43, 44, 52, 56, 62, 65], "new": [2, 4, 5, 7, 8, 13, 17, 21, 23, 25, 26, 27, 29, 30, 32, 34, 36, 38, 40, 41, 44, 47, 54, 58, 61, 62, 65, 67, 68], "manufactur": [2, 8, 17, 27, 33, 40, 62], "facil": [2, 6, 16, 21, 26, 27, 33, 38, 44], "suppli": [2, 4, 30, 32, 33, 34, 38, 56, 65], "rout": [2, 29, 50, 52, 61], "price": [2, 3, 4, 10, 11, 12, 18, 29, 31, 32, 36, 38, 48, 51, 58, 59, 61, 62, 63, 67, 68], "should": [2, 3, 8, 14, 15, 18, 22, 23, 24, 25, 26, 29, 33, 35, 36, 38, 41, 46, 49, 52, 54, 56, 58, 62, 63, 64, 65, 67, 68], "sell": [2, 3, 11, 15, 18, 26, 29, 31, 32, 37, 38, 58, 59, 61, 62, 63], "product": [2, 5, 6, 16, 20, 23, 33, 34, 35, 36, 38, 41, 44, 45, 49, 50, 53, 56, 60, 63, 64, 65], "function": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 66, 68], "evalu": [2, 5, 13, 41, 49, 62], "specif": [2, 3, 5, 6, 8, 10, 13, 14, 15, 17, 20, 25, 29, 30, 33, 34, 36, 37, 38, 40, 41, 44, 47, 52, 54, 56, 58, 59, 61, 62, 64, 66, 67, 68], "i": [2, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 64, 65, 68], "e": [2, 5, 7, 18, 19, 21, 22, 25, 27, 29, 30, 32, 33, 34, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 52, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "valu": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 64, 65, 67, 68], "earlier": [2, 5, 10, 21, 41, 44, 47, 51, 56, 58], "For": [2, 3, 5, 8, 10, 14, 16, 18, 20, 21, 22, 24, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "specifi": [2, 4, 5, 8, 13, 21, 24, 27, 29, 30, 31, 33, 36, 37, 38, 41, 44, 45, 47, 49, 57, 65, 67, 68], "either": [2, 3, 4, 5, 20, 21, 22, 23, 24, 26, 27, 29, 34, 49, 58, 62, 64, 68], "maxim": [2, 3, 4, 5, 6, 8, 9, 10, 17, 18, 20, 23, 26, 29, 30, 31, 34, 36, 37, 38, 40, 45, 46, 47, 48, 54, 57, 59, 61, 62, 63, 67, 68], "minim": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 20, 21, 22, 24, 25, 27, 29, 33, 34, 36, 39, 41, 43, 44, 49, 51, 52, 54, 56, 63, 64, 65, 68], "In": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68], "oper": [2, 3, 15, 21, 22, 27, 29, 34, 36, 38, 40, 41, 43, 44, 52, 58, 61, 62, 64, 65, 67], "cost": [2, 3, 4, 5, 6, 11, 12, 14, 15, 18, 20, 26, 28, 29, 30, 31, 34, 36, 38, 40, 43, 44, 51, 52, 56, 58, 59, 62, 63, 64, 65, 67], "number": [2, 3, 4, 5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 24, 25, 27, 29, 31, 34, 36, 37, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 68], "satisfi": [2, 3, 9, 15, 16, 18, 19, 20, 27, 29, 30, 31, 32, 33, 38, 41, 45, 46, 48, 56, 58, 61, 64], "custom": [2, 20, 21, 29, 32, 36, 38, 41, 50, 56, 61, 68], "restrict": [2, 16, 25, 30, 34, 45, 48, 52, 54, 67, 68], "possibl": [2, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 24, 27, 29, 30, 32, 33, 34, 37, 38, 39, 41, 46, 49, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 67, 68], "condit": [2, 3, 8, 15, 18, 23, 24, 26, 27, 29, 30, 41, 43, 49, 54, 58, 63, 67, 68], "must": [2, 3, 5, 19, 21, 23, 26, 27, 29, 31, 32, 34, 37, 38, 52, 62, 63, 64, 65, 67], "requir": [2, 3, 4, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 38, 41, 44, 46, 49, 51, 52, 56, 58, 61, 62, 63, 65, 67, 68], "maximum": [2, 3, 8, 11, 15, 18, 23, 24, 25, 29, 33, 34, 38, 43, 46, 47, 48, 52, 54, 56, 62, 65, 67], "allow": [2, 4, 8, 18, 23, 24, 27, 29, 31, 32, 34, 40, 44, 47, 48, 52, 54, 56, 64, 67], "budget": [2, 12, 36, 51, 62], "demand": [2, 3, 4, 6, 18, 20, 26, 30, 32, 33, 34, 36, 38, 44, 50, 51, 56, 58, 62, 63, 64, 65, 67, 68], "import": [2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 57, 58, 59, 62, 63, 68], "met": [2, 34, 56], "capac": [2, 12, 18, 23, 24, 30, 33, 34, 40, 51, 56, 61, 65, 68], "warehous": [2, 18, 44, 50, 59, 68], "exceed": [2, 29, 30, 38, 56], "both": [2, 4, 5, 8, 12, 13, 17, 20, 23, 24, 26, 27, 29, 34, 38, 41, 44, 46, 49, 54, 60, 61, 62], "express": [2, 3, 4, 7, 8, 9, 10, 11, 12, 16, 18, 19, 22, 26, 29, 30, 32, 33, 34, 38, 40, 41, 43, 44, 45, 48, 49, 51, 54, 56, 59, 63, 64, 65, 67, 68], "defin": [2, 4, 5, 8, 9, 12, 13, 14, 16, 20, 22, 25, 26, 27, 32, 34, 36, 38, 39, 40, 41, 44, 45, 49, 52, 54, 56, 61, 62, 64, 65, 67, 68], "feasibl": [2, 5, 8, 9, 17, 20, 30, 34, 36, 40, 43, 45, 46, 48, 52, 59, 62, 64, 65], "region": [2, 8, 20, 32, 34], "set": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "candid": [2, 3, 7, 21, 36], "meet": [2, 3, 5, 12, 18, 22, 25, 29, 30, 32, 34, 36, 38, 42, 51, 56, 58, 64, 65], "best": [2, 5, 7, 8, 29, 30, 34, 43, 56, 62, 65, 68], "global": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 45, 47, 51, 54, 56, 59, 61, 65, 68], "optimum": [2, 40, 43], "among": [2, 3, 7, 11, 14, 15, 21, 22, 26, 31, 32, 34, 46, 47, 48, 61, 63, 64, 68], "appli": [2, 4, 5, 18, 24, 34, 36, 38, 41, 43, 44, 48, 64, 65, 67], "type": [2, 3, 4, 5, 6, 8, 12, 16, 17, 19, 21, 25, 29, 30, 34, 36, 41, 44, 49, 51, 54, 56, 64, 65, 67], "relat": [2, 5, 18, 23, 26, 29, 38, 49, 54, 56, 61, 62, 64], "fundament": [2, 14, 38, 67], "question": [2, 8, 15, 22, 41, 44, 50, 58, 63, 64], "translat": [2, 4, 5, 18, 22, 29, 32, 36, 43], "real": [2, 4, 5, 7, 13, 16, 21, 30, 31, 34, 35, 36, 39, 45, 46, 48, 50, 52, 54, 56, 58, 61, 62, 64], "abstract": [2, 4, 8, 67], "represent": [2, 21, 41, 43, 49], "Not": [2, 29], "aspect": [2, 6, 8, 14, 16, 28, 35, 42, 53, 55, 60], "taken": [2, 29, 62, 64, 68], "account": [2, 10, 18, 23, 24, 29, 31, 34, 42, 45, 46, 54, 58, 59, 62, 64, 65, 67], "so": [2, 3, 4, 5, 8, 9, 13, 18, 21, 23, 24, 25, 27, 29, 30, 34, 35, 36, 37, 38, 39, 41, 43, 47, 49, 50, 52, 54, 56, 61, 62, 65, 67], "mani": [2, 3, 4, 8, 16, 19, 20, 22, 23, 27, 29, 30, 31, 32, 34, 36, 41, 44, 45, 46, 48, 49, 51, 52, 54, 58, 61, 62, 64, 65, 68], "made": [2, 3, 10, 12, 18, 20, 24, 29, 51, 58, 62, 65], "step": [2, 3, 4, 8, 9, 12, 14, 15, 18, 21, 29, 44, 45, 51, 52, 61, 62, 67, 68], "typic": [2, 21, 34, 56, 58, 62, 67, 68], "signific": [2, 5, 8, 40, 44, 45, 46, 48, 49], "impact": [2, 7, 38, 50, 63], "approach": [2, 5, 7, 8, 10, 13, 14, 17, 19, 20, 22, 26, 27, 30, 36, 38, 43, 45, 48, 56, 62, 64, 67], "There": [2, 5, 18, 19, 22, 25, 26, 29, 30, 34, 36, 38, 43, 44, 58, 62, 67, 68], "multipl": [2, 3, 12, 15, 18, 27, 29, 31, 32, 33, 36, 38, 68], "equival": [2, 5, 8, 18, 20, 22, 41, 43, 52, 54, 57, 61, 68], "formul": [2, 3, 4, 6, 8, 9, 11, 13, 14, 15, 16, 18, 22, 23, 26, 27, 28, 29, 30, 32, 34, 35, 38, 39, 43, 48, 50, 54, 56, 58, 59, 61, 64, 65, 68], "conceptu": 2, "same": [2, 3, 4, 5, 10, 11, 13, 14, 15, 18, 19, 21, 22, 25, 27, 29, 30, 31, 32, 33, 39, 41, 44, 45, 49, 52, 54, 56, 59, 62, 64, 65, 67, 68], "comput": [2, 4, 5, 7, 8, 14, 15, 18, 22, 23, 29, 30, 31, 34, 36, 38, 41, 44, 45, 48, 49, 51, 58, 61, 63, 64, 65, 67, 68], "complex": [2, 4, 5, 15, 21, 22, 23, 29, 31, 36, 67, 68], "mai": [2, 3, 4, 5, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23, 27, 29, 31, 32, 34, 36, 38, 40, 41, 44, 45, 49, 51, 54, 56, 57, 63, 67], "differ": [2, 3, 5, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 27, 29, 30, 32, 33, 34, 36, 38, 39, 40, 41, 43, 44, 47, 48, 49, 50, 52, 54, 56, 58, 59, 62, 63, 64, 65, 68], "interpret": [2, 4, 15, 40], "s": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "after": [2, 5, 13, 23, 29, 30, 31, 39, 46, 52, 54, 58, 62, 63, 64, 65, 67], "ha": [2, 3, 4, 5, 7, 10, 12, 14, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67], "back": [2, 5, 13, 23, 29, 31, 41, 49, 62, 67], "origin": [2, 3, 4, 8, 9, 10, 13, 14, 17, 25, 27, 30, 34, 36, 38, 44, 47], "These": [2, 4, 15, 25, 27, 29, 33, 34, 38, 41, 44, 46, 48, 52, 56, 58, 61, 67, 68], "treat": [2, 21, 30, 50, 65], "continu": [2, 12, 18, 22, 23, 38, 41, 43, 49, 51, 59, 65], "process": [2, 4, 23, 29, 38, 40, 41, 44, 49, 52, 54, 67, 68], "sequenti": 2, "final": [2, 4, 5, 12, 14, 21, 22, 23, 29, 38, 44, 61, 64], "turn": [2, 3, 16, 21, 30, 35, 38, 41, 49, 52, 56, 65], "out": [2, 3, 23, 24, 27, 29, 30, 32, 35, 36, 38, 52, 56, 64, 65], "impract": [2, 29], "adjust": [2, 4, 14, 29, 34, 40, 56, 61, 64, 65], "certain": [2, 16, 29, 30, 32, 41, 44, 49, 54, 56, 61, 65], "desir": [2, 3, 12, 18, 25, 30, 35, 51, 67], "properti": [2, 8, 30, 35, 43, 45, 47, 48, 56], "cannot": [2, 8, 12, 16, 21, 22, 25, 29, 34, 43, 51, 52, 59, 61, 62, 65], "effici": [2, 10, 14, 21, 27, 29, 30, 47, 48, 49, 54, 68], "perhap": [2, 19, 32, 36], "re": [2, 68], "includ": [2, 5, 6, 14, 16, 18, 19, 21, 22, 23, 24, 27, 28, 29, 31, 32, 35, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 52, 56, 57, 61, 62, 63, 64, 65, 66, 67, 68], "well": [2, 5, 7, 13, 14, 19, 22, 27, 29, 35, 36, 43, 45, 46, 59, 63, 68], "except": [2, 20, 25, 65, 67, 68], "mathematician": 2, "studi": [2, 15, 21, 27, 36, 38, 43, 56, 59, 68], "alwai": [2, 4, 10, 12, 17, 24, 34, 37, 39, 41, 45, 46, 47, 49, 50, 54, 57, 61, 67], "flaw": 2, "challeng": [2, 8, 19, 27, 31, 32, 36, 38, 41], "follow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "given": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 38, 41, 43, 44, 45, 46, 48, 49, 50, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "f": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "x": [2, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 49, 51, 52, 54, 56, 57, 58, 59, 62, 63, 64, 65, 67, 68], "mathbb": [2, 6, 8, 13, 16, 17, 18, 26, 34, 36, 37, 39, 41, 44, 45, 46, 48, 49, 54, 56, 57, 58, 59, 61, 64, 65], "r": [2, 4, 6, 8, 13, 14, 16, 18, 19, 21, 22, 24, 25, 27, 29, 31, 32, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 57, 58, 59, 61, 62, 63, 64, 65, 67], "subseteq": [2, 16, 34, 52], "n": [2, 6, 7, 8, 9, 13, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 68], "being": [2, 5, 6, 8, 34, 37, 38, 49, 52, 65], "y": [2, 4, 7, 8, 9, 13, 14, 18, 19, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48, 49, 54, 56, 58, 59, 62, 63, 65, 67, 68], "geq": [2, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 59, 61, 62, 64, 65, 67], "foral": [2, 4, 7, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 44, 45, 47, 48, 49, 52, 54, 56, 58, 61, 62, 64, 65], "least": [2, 6, 7, 12, 18, 22, 24, 34, 35, 36, 38, 51, 52, 62, 63], "good": [2, 4, 5, 7, 15, 20, 27, 29, 32, 33, 37, 45, 48, 58, 62, 63, 68], "similarli": [2, 8, 13, 18, 34, 39], "chang": [2, 4, 5, 10, 14, 15, 21, 23, 24, 27, 29, 30, 32, 34, 37, 38, 39, 40, 47, 50, 52, 54, 56, 57, 61, 62, 64, 67, 68], "last": [2, 12, 18, 22, 23, 24, 29, 34, 37, 38, 51, 52, 54, 63, 65], "leq": [2, 3, 4, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 61, 62, 64, 65, 68], "case": [2, 3, 4, 5, 7, 8, 13, 16, 20, 21, 25, 26, 27, 29, 30, 32, 36, 39, 41, 45, 46, 48, 49, 54, 56, 58, 59, 65, 67, 68], "refer": [2, 3, 4, 6, 8, 13, 16, 20, 24, 27, 29, 32, 33, 38, 39, 41, 46, 49, 64, 68], "begin": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "align": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "min": [2, 6, 7, 8, 9, 11, 13, 14, 16, 18, 20, 22, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 41, 43, 44, 45, 49, 52, 54, 56, 58, 59, 62, 64, 65, 68], "quad": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 37, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 57, 58, 59, 61, 62, 64, 67, 68], "text": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "t": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "end": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "lead": [2, 14, 17, 18, 19, 20, 21, 22, 26, 29, 35, 38, 41, 43, 49, 54, 56, 62, 65, 68], 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46, 68], "lastli": [2, 3, 61, 62, 65], "discuss": [2, 32, 38, 41, 44, 49, 50, 62], "some": [2, 3, 4, 5, 7, 8, 13, 16, 21, 23, 27, 29, 30, 32, 34, 38, 39, 40, 41, 43, 45, 46, 48, 49, 54, 56, 62, 64, 65, 67, 68], "advanc": [2, 3, 5, 23, 38, 44, 50, 62, 64, 65], "featur": [2, 4, 5, 6, 8, 13, 16, 20, 21, 22, 23, 27, 33, 38, 39, 46, 48, 59, 61, 67, 68], "third": [2, 4, 5, 32], "go": [2, 6, 8, 13, 14, 16, 21, 28, 30, 34, 35, 38, 39, 42, 50, 52, 53, 55, 62, 63, 64, 67], "next": [2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 21, 22, 23, 25, 28, 29, 30, 32, 33, 35, 37, 42, 45, 49, 50, 52, 53, 55, 56, 59, 63, 64, 65], "about": [2, 6, 8, 16, 20, 24, 28, 29, 30, 35, 38, 42, 50, 53, 55, 56, 58, 61, 62, 64], "linear": [2, 3, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 19, 20, 22, 23, 26, 29, 31, 34, 35, 38, 39, 40, 54, 56, 59, 60, 61, 62, 64], "compani": [3, 6, 8, 12, 17, 18, 33, 38, 44, 50, 51, 52, 54, 62], "produc": [3, 4, 7, 8, 10, 12, 15, 17, 21, 23, 26, 29, 31, 32, 34, 36, 38, 41, 45, 46, 51, 52, 54, 56, 62, 64, 65, 67], "two": [3, 4, 5, 6, 8, 12, 13, 14, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 32, 36, 38, 41, 44, 46, 49, 52, 54, 55, 56, 58, 59, 64, 67, 68], "version": [3, 4, 5, 10, 18, 20, 29, 30, 32, 34, 38, 44, 52, 53, 54, 62, 64, 65, 68], "each": [3, 4, 5, 8, 9, 12, 13, 14, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 41, 45, 46, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "raw": [3, 5, 8, 12, 18, 26, 29, 34, 38, 41, 49, 51, 56, 62, 64, 65], "10": [3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "per": [3, 8, 10, 12, 14, 18, 23, 24, 25, 26, 29, 32, 33, 36, 43, 51, 59, 62, 63, 64], "gram": [3, 18, 41, 49], "special": [3, 4, 11, 12, 13, 16, 28, 30, 36, 39, 44, 61, 68], "labor": [3, 4, 26, 62, 67], "finish": [3, 12, 18, 21, 22, 23, 26, 36, 51, 52, 68], "u": [3, 4, 5, 14, 18, 23, 29, 33, 41, 43, 44, 45, 48, 49, 52, 54, 56, 62, 67, 68], "higher": [3, 14, 26, 29, 38, 41, 46, 49, 51, 56, 58, 62, 64], "270": [3, 4, 5, 26, 29, 62, 65, 67], "unit": [3, 4, 5, 10, 12, 14, 15, 18, 23, 24, 26, 29, 31, 32, 33, 34, 38, 43, 44, 46, 51, 56, 57, 58, 59, 61, 62, 63, 64, 67], "one": [3, 4, 5, 6, 8, 9, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 61, 62, 63, 64, 65], "hour": [3, 4, 21, 24, 25, 26, 52, 62], "b": [3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 19, 21, 22, 25, 26, 29, 32, 33, 34, 36, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 52, 59, 61, 62, 64, 65, 67, 68], "due": [3, 4, 12, 14, 18, 29, 34, 37, 38, 50, 51, 54, 58, 61, 63, 64, 68], "market": [3, 4, 14, 15, 29, 31, 38, 45, 46, 48, 61, 62, 63], "limit": [3, 5, 12, 18, 20, 21, 24, 25, 26, 29, 32, 34, 36, 38, 40, 43, 45, 48, 51, 56, 62, 64, 65, 68], "40": [3, 4, 5, 7, 24, 25, 26, 29, 32, 36, 43, 44, 45, 58, 61, 62, 63, 67, 68], "week": [3, 25, 61], "v": [3, 4, 5, 14, 20, 21, 24, 29, 30, 33, 34, 38, 41, 44, 45, 49, 52, 56, 62, 64, 65, 67, 68], "lower": [3, 4, 5, 12, 14, 21, 29, 31, 32, 36, 38, 39, 44, 54, 58, 61, 63, 64, 68], "unlimit": [3, 4, 26, 36, 38, 62], "210": [3, 4, 5, 26, 61, 62, 63, 67], "9": [3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 44, 50, 51, 52, 54, 61, 62, 65, 67], "1": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 64, 65, 67, 68], "summar": [3, 4, 25, 32, 34, 38, 41, 49, 58, 61, 62, 63], "tabl": [3, 4, 18, 21, 22, 24, 26, 27, 31, 41, 45, 51, 52, 54, 58, 59, 61, 63, 68], "g": [3, 4, 5, 8, 14, 18, 20, 22, 27, 29, 30, 31, 32, 33, 34, 38, 39, 41, 43, 44, 49, 52, 54, 59, 64, 65, 67], "hr": [3, 4, 24, 52], "2": [3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68], "weekli": [3, 25], "avail": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 44, 45, 47, 48, 51, 52, 54, 56, 58, 59, 61, 62, 65, 67], "inventori": [3, 12, 18, 29, 44, 51, 58, 59], "shelf": 3, "life": [3, 22, 30, 35, 43, 52], "order": [3, 4, 10, 12, 18, 21, 22, 25, 30, 32, 34, 36, 40, 41, 42, 49, 51, 52, 58, 62], "left": [3, 7, 8, 9, 10, 12, 13, 21, 22, 23, 24, 25, 27, 30, 32, 36, 38, 39, 41, 43, 44, 46, 48, 49, 51, 52, 56, 59, 61, 62, 64], "over": [3, 4, 10, 11, 14, 18, 21, 23, 25, 29, 30, 34, 36, 38, 40, 41, 44, 46, 49, 51, 58, 61, 62, 63, 67, 68], "discard": 3, "detail": [3, 8, 20, 25, 41, 43, 49, 58], "resourc": [3, 4, 15, 21, 25, 27, 29, 30, 36, 51, 63, 64, 67], "amount": [3, 4, 12, 15, 16, 17, 18, 21, 22, 25, 29, 31, 32, 33, 37, 38, 50, 51, 54, 56, 57, 59, 62, 63, 64, 68], "80": [3, 4, 5, 26, 29, 32, 34, 40, 46, 62, 63, 65, 67], "50": [3, 4, 5, 9, 14, 18, 24, 26, 29, 31, 32, 34, 36, 38, 41, 43, 44, 48, 51, 54, 59, 61, 62, 63, 65, 67, 68], "100": [3, 4, 5, 10, 18, 19, 20, 22, 24, 26, 29, 31, 32, 34, 36, 38, 40, 41, 44, 46, 48, 49, 51, 54, 56, 59, 61, 62, 63, 64, 65, 67, 68], "want": [3, 14, 17, 18, 20, 29, 37, 39, 41, 47, 54, 57, 61, 62, 68], "its": [3, 5, 7, 8, 9, 13, 14, 20, 22, 30, 33, 39, 41, 44, 49, 54, 62, 64, 67, 68], "gross": [3, 45, 46, 48], "profit": [3, 4, 5, 6, 8, 9, 10, 17, 23, 26, 29, 32, 36, 38, 40, 54, 61, 62, 63, 67, 68], "much": [3, 8, 12, 13, 17, 18, 23, 24, 27, 30, 36, 43, 46, 50, 62, 64, 65], "abov": [3, 4, 6, 8, 11, 12, 13, 14, 17, 18, 22, 23, 24, 29, 30, 32, 34, 36, 38, 39, 43, 44, 45, 47, 48, 49, 51, 54, 56, 59, 61, 62, 64, 68], "optim": [3, 4, 5, 9, 10, 11, 13, 17, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 39, 40, 44, 45, 51, 52, 54, 56, 58, 60, 66, 68], "reformul": [3, 5, 10, 13, 14, 43, 45, 46, 48, 49, 54, 61], "involv": [3, 5, 6, 8, 11, 21, 31, 38, 41, 44, 45, 46, 48], "few": [3, 4, 5, 32, 41, 61, 67], "crucial": [3, 34], "element": [3, 4, 5, 8, 25, 30, 32, 41, 43, 46, 49, 64, 67, 68], "start": [3, 5, 7, 8, 13, 14, 18, 21, 22, 23, 24, 27, 29, 31, 36, 48, 54, 61, 62], "point": [3, 4, 7, 8, 13, 16, 20, 23, 27, 29, 33, 38, 39, 40, 41, 44, 46, 49, 54, 59, 61, 65, 68], "list": [3, 4, 8, 14, 19, 21, 24, 25, 26, 29, 30, 31, 34, 36, 41, 43, 45, 48, 49, 51, 52, 56, 59, 61, 62, 64, 67, 68], "relev": [3, 4, 18, 20, 27, 29, 65, 67, 68], "hand": [3, 4, 8, 9, 17, 20, 24, 25, 29, 30, 31, 41, 49, 54, 56, 58], "quantiti": [3, 8, 11, 12, 18, 33, 34, 36, 38, 42, 50, 51, 54, 62], "modifi": [3, 5, 17, 20, 21, 23, 29, 31, 41, 54, 56, 61, 64, 65, 67, 68], "achiev": [3, 11, 14, 23, 25, 37, 41, 49, 64, 67], "outcom": [3, 15, 32, 41, 45, 49, 56, 58, 62], "while": [3, 5, 8, 9, 12, 14, 17, 18, 20, 21, 24, 25, 26, 29, 33, 34, 36, 41, 47, 48, 49, 50, 51, 52, 54, 56, 58, 61, 62, 64, 65], "stage": [3, 5, 27, 38, 45, 46, 59, 63, 64, 67, 68], "prove": [3, 8], "redund": [3, 68], "later": [3, 4, 5, 15, 21, 24, 27, 44, 46, 65, 68], "creat": [3, 12, 13, 14, 15, 19, 21, 22, 23, 27, 29, 30, 31, 34, 36, 38, 41, 44, 45, 48, 49, 56, 58, 61, 62, 63, 67, 68], "comprehens": [3, 7, 41, 49], "below": [3, 5, 9, 11, 12, 14, 17, 20, 21, 24, 29, 30, 34, 36, 38, 41, 43, 44, 45, 49, 54, 56, 59, 61, 62, 65, 68], "symbol": [3, 14, 22, 29, 31, 32, 48, 58, 67, 68], "descript": [3, 15, 21, 32, 34, 41, 50, 54, 65], "upper": [3, 4, 5, 9, 12, 14, 21, 22, 23, 24, 29, 32, 36, 37, 38, 39, 43, 51, 54, 59, 67, 68], "bound": [3, 4, 5, 9, 14, 19, 21, 22, 23, 24, 26, 29, 30, 31, 36, 37, 38, 41, 43, 52, 54, 56, 62, 63, 65, 67], "known": [3, 4, 5, 7, 13, 14, 19, 21, 22, 23, 27, 37, 38, 39, 41, 43, 44, 46, 48, 49, 52, 56, 58, 62, 63, 64, 65], "x_m": [3, 5, 59], "0": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "x_a": [3, 5], "x_b": [3, 5], "y_u": [3, 5], "y_v": [3, 5], "measur": [3, 7, 10, 14, 23, 24, 25, 29, 38, 41, 49, 50], "max": [3, 4, 7, 8, 9, 10, 11, 14, 15, 17, 21, 22, 23, 24, 25, 26, 27, 29, 34, 36, 37, 38, 40, 41, 43, 45, 46, 47, 49, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "equal": [3, 5, 6, 7, 8, 10, 12, 15, 16, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 43, 44, 45, 46, 48, 52, 54, 56, 58, 59, 61, 62, 64, 65], "between": [3, 4, 5, 8, 10, 13, 14, 20, 21, 24, 25, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 43, 45, 46, 47, 49, 52, 56, 63, 64, 65, 67, 68], "revenu": [3, 4, 5, 10, 11, 40, 58, 61, 63, 67], "written": [3, 4, 5, 15, 41, 44, 57, 58, 61, 67, 68], "As": [3, 4, 5, 8, 14, 18, 21, 25, 29, 30, 31, 38, 41, 44, 45, 49, 52, 58, 59, 62, 65, 68], "shown": [3, 5, 38, 41, 44, 45, 68], "here": [3, 4, 5, 10, 12, 13, 21, 22, 24, 25, 26, 29, 31, 38, 39, 41, 44, 45, 46, 48, 51, 52, 54, 56, 57, 58, 61, 62, 65, 67, 68], "algebra": [3, 5, 19, 40, 41, 44], "combin": [3, 4, 5, 18, 19, 29, 38, 43, 52, 56, 62, 64, 67, 68], "name": 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"canon": [3, 6, 37], "nonumb": [3, 4, 30], "textbook": [3, 20, 62, 63], "customari": [3, 8, 49], "under": [3, 14, 15, 18, 20, 25, 26, 51, 56, 58, 61, 62, 63, 67], "clearli": [3, 4, 7, 18, 20, 23, 25, 38, 52, 62, 65], "distinguish": [3, 29, 65, 68], "paramet": [3, 4, 5, 18, 21, 23, 29, 32, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 54, 56, 58, 59, 61, 62, 64, 65, 67], "might": [3, 15, 16, 27, 29, 31, 34, 35, 38, 41, 50, 58, 64, 67], "letter": [3, 19, 44, 62], "throughout": [3, 8, 29, 62, 68], "websit": 3, "howev": [3, 4, 8, 13, 15, 16, 17, 18, 20, 28, 29, 30, 31, 34, 35, 38, 41, 43, 44, 47, 48, 49, 52, 54, 56, 58, 61, 62, 64, 65, 68], "stick": 3, "convent": [3, 5, 6, 8, 56, 64, 65], "onli": [3, 4, 7, 8, 9, 16, 18, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 38, 41, 48, 52, 54, 56, 58, 61, 62, 63, 64, 68], "explicitli": [3, 8, 17, 29, 30, 31, 49, 52, 61, 63, 68], "domain": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67], "vector": [3, 6, 8, 11, 12, 13, 34, 35, 37, 39, 40, 42, 57, 61, 62], "minimum": [3, 11, 12, 14, 16, 21, 24, 25, 28, 29, 34, 36, 38, 43, 52, 61, 63], "even": [3, 5, 20, 25, 29, 30, 38, 41, 47, 49, 52, 61, 62, 64, 65], "like": [3, 4, 8, 9, 11, 12, 14, 18, 29, 30, 31, 32, 36, 38, 41, 49, 51, 61, 62, 64, 65], "immedi": [3, 21, 29], "clear": [3, 9, 17, 35, 56, 62, 68], "what": [3, 8, 15, 21, 22, 25, 29, 31, 38, 40, 42, 45, 46, 50, 54, 56, 58, 61, 62, 63, 65, 67], "exactli": [3, 14, 33, 34, 44, 49, 50, 52, 54, 61, 62, 65], "where": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67, 68], "come": [3, 4, 14, 15, 30, 35, 41, 49, 62], "plai": [3, 8, 35, 45, 49, 52, 62], "thei": [3, 5, 8, 16, 18, 22, 24, 29, 30, 31, 32, 35, 36, 38, 41, 48, 50, 52, 56, 61, 62, 64, 65, 67, 68], "procedur": [3, 36, 54, 62], "long": [3, 4, 14, 16, 22, 25, 29, 45, 46, 59, 62, 68], "standard": [3, 5, 6, 8, 13, 14, 22, 29, 34, 38, 40, 41, 49, 57, 61, 68], "fashion": [3, 4, 52], "practition": [3, 48], "often": [3, 4, 16, 21, 28, 32, 34, 50, 62, 65, 68], "boil": 3, "down": [3, 17, 23, 29, 30, 34, 46, 61, 62, 63], "pass": [3, 4, 5, 18, 20, 21, 24, 28, 41, 49, 67], "softwar": [3, 26], "regardless": [3, 4, 8, 12, 18, 29, 45, 51, 59], "wa": [3, 7, 14, 25, 29, 30, 32, 34, 35, 36, 38, 44, 45, 46, 47, 48, 52, 54, 58, 62, 64], "stori": [3, 45, 46], "behind": 3, "To": [3, 5, 8, 18, 21, 22, 23, 24, 25, 27, 29, 30, 34, 36, 38, 41, 43, 48, 49, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67], "do": [3, 4, 8, 20, 23, 25, 29, 30, 31, 34, 38, 40, 44, 52, 54, 56, 61, 62, 64, 65, 67, 68], "interfac": [3, 4, 5, 14, 44, 68], "commun": [3, 5, 7, 20, 25, 27, 63], "adopt": [3, 8, 26, 32, 41, 43, 46, 54], "python": [3, 5, 8, 13, 17, 20, 21, 25, 29, 30, 32, 36, 39, 40, 41, 44, 49, 52, 56, 65, 67, 68], "base": [3, 5, 7, 12, 13, 19, 21, 26, 29, 32, 34, 39, 40, 41, 46, 48, 49, 50, 51, 56, 62, 64, 67, 68], "carri": [3, 24, 32, 34], "ll": [4, 31, 38], "revisit": [4, 16, 25, 42, 60], "time": [4, 5, 6, 8, 14, 18, 20, 23, 24, 25, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 48, 49, 50, 51, 52, 56, 58, 61, 62, 64, 65, 67, 68], "capabl": [4, 7, 26, 38, 54], "scale": [4, 7, 29, 30, 34, 36, 38, 41, 56, 59, 61, 62, 65], "enabl": [4, 5, 28, 29, 44, 49, 67], "vari": [4, 12, 37, 49, 51, 54, 56, 63], "addit": [4, 6, 7, 12, 15, 18, 21, 23, 25, 27, 29, 36, 41, 44, 46, 48, 49, 58, 61, 62, 65, 67, 68], "compon": [4, 5, 8, 13, 14, 18, 22, 26, 37, 44, 49, 57, 68], "index": [4, 5, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 38, 39, 40, 41, 43, 44, 48, 49, 51, 52, 56, 58, 61, 63, 64, 67], "essenti": [4, 22, 29, 30, 36, 41, 49, 52, 54, 62], "scalabl": [4, 6], "maintain": [4, 5, 8, 27, 29, 44, 65, 68], "more": [4, 5, 8, 10, 11, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 34, 36, 37, 38, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "analysi": [4, 7, 8, 12, 13, 21, 29, 32, 38, 41, 45, 46, 49, 50, 52, 54, 61, 68], "examin": [4, 7, 17, 38, 48], "identifi": [4, 5, 16, 18, 24, 27, 29, 31, 33, 36, 38, 41, 43, 46, 49, 61, 65, 68], "underli": [4, 39, 62], "verifi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 49, 51, 52, 54, 56, 58, 59, 61, 65], "high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 38, 41, 43, 48, 49, 51, 54, 56, 58, 59, 62, 64, 65, 68], "via": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 49, 50, 54, 56, 59, 65], "appsi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 51, 54, 59, 65], "modul": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "elsewher": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 37, 47, 51, 54, 56, 59, 65, 68], "assum": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 41, 43, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "cbc": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 38, 40, 51, 52, 54, 58, 59, 61, 62, 63, 64, 65, 67], "been": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 59, 61, 63, 65, 67, 68], "previous": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 37, 46, 47, 49, 51, 52, 54, 56, 59, 61, 64, 65, 67, 68], "solverfactori": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "It": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 44, 47, 51, 54, 56, 59, 62, 64, 65], "sy": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "pip": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "dev": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65], "null": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65], "highspi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 40, 51, 52, 54, 58, 59, 61, 62, 63, 64, 65], "environ": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "appsi_high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 40, 52, 54, 58, 59, 61, 62, 63, 64, 65], "els": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 31, 32, 33, 34, 37, 41, 44, 46, 47, 49, 51, 52, 54, 59, 61, 62, 64, 65, 67, 68], "assert": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 51, 54, 56, 59], "basic": [4, 16, 17, 21, 27, 41, 54, 64, 67, 68], "previou": [4, 9, 12, 15, 18, 28, 29, 34, 41, 44, 49, 50, 62], "were": [4, 18, 27, 30, 34, 41, 48, 61, 64], "distinct": [4, 22, 29], "evid": [4, 20], "compris": [4, 21, 23, 25, 31, 36, 39, 58, 67, 68], "abbrevi": [4, 29, 67], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "factor": [4, 7, 20, 41, 43, 44, 47, 49, 54, 61, 64, 68], "attribut": [4, 5, 7, 14, 29, 34, 36, 44, 64, 67, 68], "built": [4, 20, 56], "librari": [4, 5, 8, 13, 21, 29, 30, 32, 39, 40, 41, 43, 44, 46, 48, 49, 68], "handl": [4, 23, 26, 44, 67], "tabular": [4, 5], "sever": [4, 5, 6, 7, 11, 16, 19, 24, 26, 28, 29, 35, 36, 38, 41, 48, 56, 68], "option": [4, 5, 14, 21, 27, 29, 40, 41, 49, 62, 64, 67, 68], "would": [4, 7, 8, 12, 15, 17, 18, 19, 21, 25, 26, 29, 31, 32, 34, 38, 41, 44, 47, 48, 49, 51, 52, 54, 56, 58, 61, 62, 63, 64, 65, 67], "appropri": [4, 5, 41, 49], "task": [4, 16, 20, 24, 29, 30, 32, 36, 41, 52], "nest": [4, 36, 62, 67, 68], "dictionari": [4, 18, 21, 29, 33, 34, 36, 41, 44, 49, 52, 56, 62, 68], "column": [4, 5, 7, 12, 14, 15, 18, 20, 22, 24, 25, 29, 30, 31, 32, 34, 38, 41, 44, 48, 49, 51, 52, 61, 63, 65, 68], "show": [4, 5, 7, 9, 12, 13, 14, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 43, 44, 46, 47, 50, 51, 52, 54, 56, 58, 59, 61, 62, 65, 67, 68], "repres": [4, 6, 8, 18, 19, 22, 23, 25, 31, 33, 34, 36, 38, 41, 43, 44, 45, 46, 48, 49, 56, 58, 67, 68], "kei": [4, 5, 8, 14, 20, 21, 29, 32, 33, 34, 36, 39, 45, 48, 51, 52, 56, 58, 59, 62, 64, 65, 67, 68], "outermost": 4, "inner": [4, 41, 49], "float": [4, 30, 41, 49, 51, 59, 68], "none": [4, 5, 7, 20, 21, 27, 29, 30, 32, 33, 34, 39, 41, 44, 46, 49, 51, 56, 65, 67], "print": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67], "item": [4, 21, 30, 32, 33, 34, 36, 49, 52, 56, 58, 59, 64, 65, 68], "consum": [4, 21, 34, 56, 64, 68], "8s": [4, 29, 48], "row": [4, 5, 7, 8, 22, 29, 31, 34, 41, 52, 62, 65, 68], "label": [4, 7, 13, 15, 24, 27, 29, 31, 32, 33, 34, 36, 38, 39, 40, 41, 43, 44, 49, 51, 52, 56, 59, 65, 68], "4s": 4, "By": [4, 5, 7, 14, 30, 31, 32, 54, 68], "rearrang": [4, 41], "straightforward": [4, 18, 54, 68], "becom": [4, 8, 9, 14, 19, 20, 22, 25, 37, 38, 40, 41, 44, 46, 48, 49, 56, 57, 58, 59, 64, 65], "collect": [4, 5, 6, 7, 11, 13, 15, 20, 21, 27, 31, 32, 34, 39, 41, 49, 61, 62, 68], "compar": [4, 7, 8, 13, 14, 19, 20, 22, 30, 36, 38, 39, 41, 44, 46, 49, 58, 59, 61, 63, 65], "adapt": [4, 15, 48, 56, 61, 62, 64], "let": [4, 7, 8, 9, 11, 12, 15, 19, 20, 21, 25, 27, 29, 30, 33, 34, 36, 37, 38, 43, 44, 46, 49, 51, 54, 56, 57, 58, 61, 62, 64, 67], "cal": [4, 49], "p": [4, 11, 12, 18, 23, 24, 26, 34, 36, 38, 40, 41, 43, 45, 46, 48, 49, 51, 52, 56, 57, 59, 61, 64, 65, 67], "respect": [4, 5, 8, 14, 18, 21, 26, 27, 29, 30, 32, 33, 34, 36, 38, 41, 45, 54, 56, 61, 62, 63, 65, 67], "x_r": [4, 38], "y_p": 4, "denot": [4, 8, 12, 15, 18, 19, 21, 23, 26, 27, 29, 30, 31, 34, 36, 38, 39, 48, 52, 56, 61, 64, 68], "constrain": [4, 12, 15, 16, 29, 34, 38, 44, 45, 48], "zero": [4, 13, 19, 29, 34, 36, 39, 40, 41, 43, 44, 49, 51, 59, 62, 64, 65], "b_r": 4, "b_p": 4, "b_q": 4, "don": [4, 8, 29, 56, 65, 68], "insert": [4, 5, 32, 44, 46], "larger": [4, 8, 20, 27, 30, 36, 41, 46, 49, 52, 58], "than": [4, 5, 8, 10, 12, 15, 16, 17, 20, 21, 23, 24, 25, 27, 29, 30, 32, 34, 36, 38, 41, 45, 46, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 64], "ever": [4, 41, 49], "design": [4, 5, 14, 19, 22, 27, 32, 38, 39, 41, 42, 65, 67], "caus": [4, 5, 38, 67], "ignor": [4, 34, 41, 49, 56], "befor": [4, 13, 20, 21, 22, 30, 38, 39, 41, 45, 52, 56, 58, 59, 61, 62, 64, 65], "sum_": [4, 7, 12, 13, 14, 15, 18, 20, 23, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 56, 57, 58, 61, 62, 64, 65, 68], "c_p": 4, "c_r": 4, "a_": [4, 15, 25, 29, 31, 36], "put": [4, 41, 58, 62, 64], "piec": [4, 21, 36], "flexibl": [4, 32, 34, 35, 62, 67], "particular": [4, 14, 16, 21, 25, 30, 34, 36, 38, 39, 44, 54, 61, 62, 64, 65, 68], "hold": [4, 5, 8, 9, 12, 14, 16, 18, 24, 27, 29, 34, 44, 51, 56, 58, 59, 61, 62, 64, 65], "scienc": [4, 5, 25, 32, 43, 44, 45, 61, 63], "see": [4, 5, 20, 21, 25, 26, 30, 31, 39, 43, 44, 45, 47, 49, 54, 56, 59, 62, 63, 64, 65, 67], "facilit": [4, 8, 14, 68], "construct": [4, 5, 7, 9, 14, 18, 21, 29, 32, 36, 41, 43, 44, 49, 52, 64, 65, 67, 68], "pyo": [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "initi": [4, 7, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 45, 47, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "assign": [4, 5, 15, 16, 19, 22, 24, 25, 29, 30, 31, 32, 36, 40, 41, 49, 50, 65], "decor": [4, 5, 8, 9, 25, 32, 61], "declar": [4, 5, 34, 64, 65, 68], "return": [4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "form": [4, 6, 22, 23, 35, 43, 44, 50, 57, 59, 62, 64, 67], "param": [4, 7, 12, 14, 15, 18, 21, 22, 23, 24, 25, 27, 29, 30, 32, 38, 41, 51, 56, 59, 64, 67, 68], "omit": [4, 68], "emb": 4, "extern": [4, 21, 43, 54], "directli": [4, 5, 7, 14, 22, 23, 32, 34, 43, 44, 49, 58], "effect": [4, 5, 21, 23, 33, 43, 45, 46, 52, 67], "doe": [4, 5, 15, 21, 23, 25, 27, 29, 30, 34, 36, 39, 41, 44, 49, 54, 61, 65], "keep": [4, 12, 18, 25, 34, 38, 49, 52, 62, 64, 68], "shorter": 4, "remov": [4, 6, 8, 15, 29, 36, 41, 52], "overhead": 4, "blur": [4, 29], "boundari": [4, 27, 68], "statement": [4, 30, 32, 61], "edit": [4, 36, 45], "locat": [4, 16, 24, 29, 32, 33, 38, 40, 41, 56, 58, 68], "portion": [4, 48], "therebi": 4, "significantli": [4, 8, 27, 30, 38, 41, 49], "improv": [4, 5, 25, 41, 43, 44, 46, 63], "maintainabi": 4, "concern": [4, 30], "overkil": 4, "small": [4, 5, 8, 12, 25, 26, 29, 30, 31, 33, 36, 38, 40, 62, 64, 68], "consider": [4, 14, 15, 21, 25, 32, 41, 58, 68], "note": [4, 5, 9, 11, 12, 13, 14, 15, 17, 18, 20, 24, 25, 29, 32, 33, 34, 36, 39, 40, 41, 47, 54, 56, 59, 61, 65, 67, 68], "becaus": [4, 8, 11, 18, 21, 25, 27, 29, 30, 32, 34, 36, 38, 41, 44, 49, 52, 54, 56, 62, 64, 67, 68], "take": [4, 5, 7, 8, 9, 11, 16, 17, 18, 21, 22, 23, 24, 27, 29, 31, 34, 37, 38, 41, 46, 47, 49, 50, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67], "logic": [4, 5, 8, 12, 17, 18, 19, 22, 26, 29, 51, 54, 68], "def": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "coeffici": [4, 6, 7, 8, 13, 15, 17, 18, 29, 39, 41, 43, 49, 54, 61, 62], "cp": [4, 40], "cr": 4, "argumentn": 4, "var": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "fix": [4, 5, 6, 7, 12, 14, 24, 25, 27, 30, 32, 34, 38, 39, 43, 44, 45, 46, 48, 56, 59, 61, 62, 63, 64, 65, 67], "done": [4, 5, 21, 22, 29, 38, 41, 58, 62], "parlanc": 4, "sometim": [4, 35, 45, 57], "call": [4, 8, 24, 27, 29, 31, 32, 34, 37, 38, 39, 41, 47, 48, 49, 52, 58, 61, 65, 67], "rule": [4, 12, 29, 38, 44, 59, 60, 62, 64, 68], "lambda": [4, 18, 19, 20, 21, 22, 25, 27, 29, 31, 34, 38, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 59, 64, 65, 67, 68], "argument": [4, 5, 18, 41, 49, 54, 61, 62, 64, 67, 68], "member": [4, 30, 32], "tupl": [4, 5, 8, 21, 25, 29, 36, 67], "quicksum": [4, 12, 18, 30, 51, 54], "accept": [4, 5, 21, 22, 29, 32, 41, 45, 48, 56, 62, 68], "success": [4, 7, 46, 67, 68], "sum": [4, 7, 8, 9, 13, 14, 15, 19, 20, 21, 22, 23, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "sens": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "Then": [4, 22, 33, 34, 41, 49, 52, 54, 61, 64, 65, 67], "associ": [4, 21, 24, 26, 32, 33, 34, 36, 39, 67, 68], "manner": [4, 32], "block": [4, 25, 36, 38, 43, 44, 45, 46, 48, 49, 54, 62, 67], "materials_us": [4, 67], "report": [4, 14, 18, 29, 30, 34, 36, 38, 41, 48, 49, 51, 54, 56, 59, 61, 67, 68], "access": [4, 21, 32, 34, 44, 56, 67], "iter": [4, 5, 18, 29, 54, 62, 67, 68], "pprint": [4, 68], "nproduct": 4, "nresourc": 4, "3": [4, 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63], "enough": [7, 8, 17, 25, 26, 29, 35, 38, 58], "replac": [7, 20, 29, 30, 31, 38, 41, 45, 49, 56, 61], "human": 7, "judgment": 7, "judg": 7, "intern": [8, 12, 18, 43, 45, 48, 51, 68], "chip": [8, 10, 12, 17, 18, 51, 54], "1g": 8, "silicon": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "plastic": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "4g": 8, "copper": [8, 9, 10, 11, 12, 15, 17, 18, 51], "memori": [8, 12, 14, 17, 18, 51, 54, 68], "germanium": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "2g": 8, "sold": [8, 29, 38, 58, 59, 61, 63], "stock": [8, 12, 18, 35, 38, 45, 46, 48, 50, 51, 55], "1000g": 8, "1500g": 8, "1750g": 8, "4800g": 8, "euro": [8, 31, 63], "12x": 8, "9y": 8, "know": [8, 29, 30, 38, 44, 49, 50, 54, 58, 61, 65], "joint": 8, "usag": [8, 68], "4x": 8, "exce": [8, 25, 30, 38, 65], "1000": [8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 20, 26, 29, 34, 44, 45, 51, 54, 56, 59, 61, 62, 63, 65], "deduc": 8, "role": [8, 29, 35, 38, 52, 54, 62], "1500": [8, 9, 10, 11, 12, 15, 17, 18, 29, 51, 54, 59, 63], "1750": [8, 9, 10, 11, 12, 15, 17, 18, 29, 32, 51, 54], "2y": [8, 68], "4800": [8, 9, 10, 11, 12, 17, 18, 44, 51, 54, 63], "leverag": [8, 30, 31, 44, 48, 54], "dimension": [8, 21, 27, 36, 41, 43, 49, 54, 61], "grai": [8, 24, 33, 34, 52], "enclos": 8, "solid": [8, 33, 64], "isolin": [8, 43], "parallel": 8, "dash": [8, 21, 22], "blue": [8, 13, 21, 34], "increas": [8, 14, 20, 21, 23, 27, 29, 31, 38, 40, 45, 46, 58, 59, 65], "intens": [8, 31, 38], "intuit": [8, 17, 46, 64], "alreadi": [8, 18, 20, 29, 31, 32, 49, 57, 65], "guess": 8, "mark": [8, 24, 29, 45, 49], "easi": [8, 9, 52, 54], "imagin": [8, 29, 30], "complic": [8, 11, 49, 68], "matter": 8, "expand": [8, 62], "obfusc": 8, "discern": 8, "analyz": [8, 28, 61, 65], "matric": [8, 62], "close": [8, 13, 14, 29, 38, 43, 48, 59], "greatli": 8, "identif": [8, 29, 31], "similar": [8, 14, 20, 38, 41, 62, 64, 68], "food": [8, 21, 25, 38], "refresh": 8, "equat": [8, 13, 18, 19, 20, 27, 29, 34, 36, 41, 44, 49, 67], "toward": [8, 25, 27], "renam": 8, "x_1": [8, 9, 10, 17, 37, 41, 43, 47, 49, 54, 57], "x_2": [8, 9, 10, 17, 37, 41, 47, 49, 54, 57], "obtain": [8, 9, 12, 13, 17, 22, 27, 29, 30, 34, 37, 38, 39, 41, 43, 44, 46, 47, 49, 54, 56, 58, 59, 61, 62, 64, 65], "pmatrix": [8, 62], "just": [8, 11, 21, 29, 32, 33, 36, 38, 43, 54, 58, 62, 64], "rewrit": [8, 13, 17, 29, 34, 39, 43, 44, 46, 47, 57, 62], "12x_1": [8, 10, 17, 54], "9x_2": [8, 10, 17, 54], "bmatrix": [8, 19, 22, 24, 26, 27, 41, 44, 49], "system": [8, 18, 29, 31, 34, 38, 45, 46, 55, 62, 65], "read": [8, 14, 21, 23, 29, 34, 36, 43, 48, 56, 61, 62, 65, 67, 68], "correctli": [8, 18, 25, 41, 54, 65], "replic": 8, "4x_1": [8, 10, 54], "longleftrightarrow": [8, 43, 44], "arrai": [8, 13, 34, 37, 39, 41, 43, 44, 47, 48, 49, 54, 56, 57, 61, 62, 64, 65, 68], "2x_2": [8, 10, 54], "x1": [8, 9, 10, 11, 17, 27, 43], "x2": [8, 9, 10, 11, 17, 27], "1f": [8, 10, 11, 15, 21, 41, 44, 45, 49, 59], "650": [8, 10, 54, 58], "1100": [8, 10, 18, 29, 54], "17700": [8, 9, 10, 54], "emploi": [8, 13, 25, 41], "enhanc": [8, 27, 38, 68], "thu": [8, 17, 18, 25, 29, 30, 34, 39, 47, 56, 59, 61, 62, 65], "util": [8, 27, 29, 32, 45, 46, 48, 52, 67], "encompass": 8, "advantag": [8, 18, 28, 30, 31, 41, 49, 67], "concis": [8, 20], "space": [8, 16, 43, 44, 52, 61, 67], "clariti": 8, "reveal": [8, 38, 49], "across": [8, 29, 33, 34, 62, 64], "uniform": [8, 23, 32, 38, 44, 45, 59, 62, 64, 68], "furthermor": [8, 18, 23, 34, 43, 68], "unnecessari": [8, 68], "theoret": [8, 51, 59], "streamlin": [8, 21, 68], "Is": [8, 15, 30, 38, 45], "answer": [8, 41, 58], "textit": 8, "sign": [8, 41, 49], "onc": [8, 22, 23, 36, 41, 49, 61, 65], "commit": [8, 31, 64], "deriv": [8, 22, 27, 34, 40, 41, 44, 49, 61], "sort": [8, 14, 20, 21, 25, 29, 41, 45, 49], "consid": [8, 11, 13, 14, 15, 18, 20, 21, 22, 24, 27, 28, 29, 30, 31, 36, 37, 38, 39, 41, 43, 45, 46, 47, 48, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 67], "anywai": [8, 30, 62], "without": [8, 15, 23, 27, 32, 36, 38, 41, 45, 46, 49, 52, 58, 62, 67, 68], "Of": [8, 16, 62], "cours": [8, 16, 17, 22, 38, 62, 68], "illustr": [8, 12, 18, 20, 21, 26, 27, 29, 30, 36, 43, 44, 51, 54, 59, 64, 65], "shall": [8, 18, 29, 43, 44, 49, 64], "upon": [8, 21, 56], "had": [8, 17, 18, 29, 41, 49, 58, 64], "mention": [8, 11], "neg": [8, 9, 17, 18, 21, 29, 33, 34, 36, 41, 44, 46, 49, 50, 56, 58, 61, 68], "therefor": [8, 14, 27, 32, 34, 37, 41, 44, 49, 61, 62, 65], "overlin": 8, "side": [8, 9, 14, 25, 29, 30, 41, 54, 56, 67], "One": [8, 9, 15, 19, 21, 22, 27, 29, 30, 34, 36, 37, 62, 64, 67], "inde": [8, 20, 25, 43, 49, 56, 62, 64], "yield": [8, 10, 17, 18, 27, 29, 38, 40, 44, 49, 51, 52, 54, 56, 62, 63, 67], "n_var": 8, "n_constraint": 8, "constraintlist": [8, 11, 59], "optimal_x": 8, "round": [8, 12, 14, 17, 18, 24, 25, 30, 31, 32, 33, 36, 37, 38, 40, 43, 44, 47, 51, 54, 61, 63, 64], "addition": [8, 57, 62], "avoid": [8, 12, 18, 19, 21, 22, 27, 32, 41, 51, 65, 67, 68], "hard": [8, 27, 29, 41, 49, 62], "doc": [8, 36, 44, 47, 68], "lambda_1": 9, "lambda_2": 9, "lambda_3": 9, "lambda_4": 9, "four": [9, 24, 38, 40, 41, 49, 62], "rh": [9, 49], "tight": [9, 20, 22, 24, 29, 51, 57], "Such": [9, 54, 56, 64, 65], "tightest": 9, "y1": [9, 10, 27, 62], "y2": [9, 10, 27, 62], "y3": [9, 62], "y4": 9, "dualiti": [9, 54], "primal": 9, "coincid": [9, 30], "ask": [9, 29, 31, 63], "suffix": [9, 32, 36, 54, 68], "component_object": [9, 54], "y_": [9, 18, 20, 23, 27, 38, 41, 49, 52, 58, 61], "y_1": [9, 10, 41, 49, 54, 62], "y_2": [9, 10, 41, 49, 54, 62], "y_3": [9, 62], "y_4": 9, "recal": [10, 12, 18, 34, 46, 47, 51, 59, 62], "pair": [10, 18, 19, 21, 22, 25, 27, 29, 31, 32, 33, 41, 52, 54, 65], "sale": [10, 14, 29, 58], "suppos": [10, 11, 13, 26, 39, 40, 54, 62], "independ": [10, 13, 34, 38, 39, 46, 48, 54, 61], "6x_1": 10, "6x_2": 10, "reason": [10, 18, 27, 29, 30, 34, 35, 41, 44, 45, 48, 49, 52, 54, 56, 59, 62, 63, 67, 68], "bim_with_revenues_minus_cost": 10, "minu": [10, 18, 33, 56, 65], "variable_cost": 10, "fixed_cost": 10, "bim_linear": 10, "3f": [10, 14, 17, 29, 37, 41, 43, 45, 46, 47, 49, 51, 56, 57, 67], "15925": 10, "000": [10, 14, 17, 32, 38, 44, 45, 54, 57, 61, 63], "1775": 10, "ratio": 10, "dfrac": 10, "deal": [10, 54], "auxiliari": [10, 11, 12, 14, 18, 22, 33, 41, 44, 46, 49, 54, 62], "12y_1": 10, "9y_2": 10, "cdot": [10, 18, 25, 26, 29, 30, 46, 49, 59, 61, 64, 65], "4y_1": 10, "2y_2": 10, "100y": 10, "despit": 10, "recov": [10, 30, 45, 64], "bim_with_revenues_over_cost": 10, "bim_fract": 10, "250": [10, 24, 34, 44, 54, 59, 63, 68], "051": [10, 29], "16500": 10, "1641": 10, "667": [10, 57], "972": 10, "strictli": [10, 52], "seemingli": 11, "rewritten": [11, 57], "maxima": [11, 38], "finit": [11, 13, 29, 39, 61, 62], "k": [11, 13, 18, 21, 22, 23, 24, 25, 27, 29, 30, 31, 33, 36, 39, 41, 43, 44, 45, 46, 48, 49, 59, 62], "c_k": 11, "minimax": 11, "max_": [11, 15, 38, 45, 46, 48, 58, 61, 68], "top_": 11, "latter": [11, 24, 65], "z": [11, 15, 16, 18, 22, 26, 29, 36, 38, 41, 43, 44, 46, 49, 54, 56, 58, 59, 61, 62], 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11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 40, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "reopen": 1, "cell": [1, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 58, 59, 61, 64, 65, 67], "insid": [1, 29, 67], "perform": [1, 5, 19, 20, 21, 22, 27, 29, 30, 45, 46, 50, 54, 58, 59, 61, 62, 63, 64, 65, 67], "ani": [1, 3, 4, 5, 6, 8, 13, 14, 15, 16, 18, 21, 22, 23, 24, 25, 27, 29, 30, 32, 34, 36, 41, 44, 46, 48, 49, 58, 59, 61, 62, 63, 64, 67, 68], "necessari": [1, 4, 27, 28, 29, 38, 50, 56, 67], "instal": [1, 36, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 52, 57, 58, 62, 63, 64, 67], "code": [1, 4, 5, 7, 8, 9, 12, 13, 17, 18, 19, 20, 24, 25, 27, 29, 30, 41, 44, 46, 48, 49, 62, 67], "within": [1, 5, 15, 20, 27, 29, 34, 54, 58, 62, 65, 67], "your": [1, 20, 23, 38, 45, 61, 63], "journei": [1, 29], "first": [1, 2, 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49, 52, 56, 62, 64, 67, 68], "krzysztof": 1, "postek": 1, "boston": 1, "consult": [1, 44, 63], "formerli": 1, "tu": 1, "delft": 1, "alessandro": 1, "zocca": 1, "vu": 1, "amsterdam": [1, 61], "joaquim": 1, "gromicho": 1, "ortec": 1, "jeffrei": 1, "kantor": 1, "notr": 1, "dame": 1, "wish": [1, 4, 14, 21, 38, 41, 61], "cite": [1, 29, 38, 44, 48], "work": [1, 5, 6, 8, 11, 20, 22, 25, 29, 30, 31, 36, 41, 45, 46, 48, 49, 56, 64, 67, 68], "postekzocca2024": 1, "titl": [1, 5, 14, 20, 22, 34, 36, 39, 41, 45, 48, 49, 52, 61, 67], "author": 1, "year": [1, 12, 18, 44, 48, 51, 63, 68], "onlin": [1, 29, 38, 65], "url": [1, 36], "http": [1, 7, 20, 29, 31, 34, 36, 38, 41, 43, 44, 45, 46, 47, 48, 49, 56, 63, 64, 65, 67], "mobook": [1, 29, 34, 41, 49, 56, 64, 65], "io": [1, 12, 18, 20, 31, 51], "mo": [1, 29, 34, 41, 49, 56, 64, 65], "broad": 2, "term": [2, 3, 4, 13, 17, 18, 20, 21, 25, 26, 29, 32, 36, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 56, 58, 59, 62, 64, 65], "describ": [2, 3, 4, 6, 8, 12, 18, 21, 22, 23, 24, 25, 27, 29, 32, 33, 34, 36, 37, 40, 41, 43, 44, 47, 48, 56, 57, 58, 61, 65, 68], "wai": [2, 3, 4, 5, 8, 11, 13, 16, 20, 21, 22, 23, 28, 29, 30, 31, 32, 33, 34, 37, 44, 45, 47, 48, 49, 52, 56, 57, 62, 64, 65, 67, 68], "them": [2, 4, 5, 8, 9, 16, 18, 24, 29, 30, 35, 37, 38, 48, 52, 54, 57, 61, 62, 64, 65], "us": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 19, 20, 21, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 56, 58, 60, 63, 64, 65, 66], "dedic": [2, 54], "algorithm": [2, 3, 27, 29, 44, 52, 62], "consist": [2, 5, 7, 13, 14, 20, 21, 25, 29, 31, 34, 39, 41, 48, 49, 61, 67, 68], "three": [2, 4, 18, 21, 22, 25, 27, 31, 38, 48, 50, 58, 59, 61, 63, 67], "part": [2, 3, 21, 36, 41, 44, 49, 54, 62, 63, 64], "correspond": [2, 3, 4, 8, 9, 11, 12, 13, 14, 17, 18, 19, 22, 23, 25, 27, 29, 34, 36, 41, 43, 44, 47, 49, 52, 54, 59, 61, 62, 64, 65, 68], "action": [2, 60, 65], "choic": [2, 4, 15, 21, 24, 26, 34, 36, 38, 41, 43, 56, 67, 68], "whether": [2, 8, 22, 24, 26, 27, 29, 31, 34, 35, 43, 44, 52, 56, 62, 65], "new": [2, 4, 5, 7, 8, 13, 17, 21, 23, 25, 26, 27, 29, 30, 32, 34, 36, 38, 40, 41, 44, 47, 54, 58, 61, 62, 65, 67, 68], "manufactur": [2, 8, 17, 27, 33, 40, 62], "facil": [2, 6, 16, 21, 26, 27, 33, 38, 44], "suppli": [2, 4, 30, 32, 33, 34, 38, 56, 65], "rout": [2, 29, 50, 52, 61], "price": [2, 3, 4, 10, 11, 12, 18, 29, 31, 32, 36, 38, 48, 51, 58, 59, 61, 62, 63, 67, 68], "should": [2, 3, 8, 14, 15, 18, 22, 23, 24, 25, 26, 29, 33, 35, 36, 38, 41, 46, 49, 52, 54, 56, 58, 62, 63, 64, 65, 67, 68], "sell": [2, 3, 11, 15, 18, 26, 29, 31, 32, 37, 38, 58, 59, 61, 62, 63], "product": [2, 5, 6, 16, 20, 23, 33, 34, 35, 36, 38, 41, 44, 45, 49, 50, 53, 56, 60, 63, 64, 65], "function": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 66, 68], "evalu": [2, 5, 13, 41, 49, 62], "specif": [2, 3, 5, 6, 8, 10, 13, 14, 15, 17, 20, 25, 29, 30, 33, 34, 36, 37, 38, 40, 41, 44, 47, 52, 54, 56, 58, 59, 61, 62, 64, 66, 67, 68], "i": [2, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 64, 65, 68], "e": [2, 5, 7, 18, 19, 21, 22, 25, 27, 29, 30, 32, 33, 34, 36, 37, 39, 40, 41, 43, 45, 46, 48, 49, 52, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "valu": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 64, 65, 67, 68], "earlier": [2, 5, 10, 21, 41, 44, 47, 51, 56, 58], "For": [2, 3, 5, 8, 10, 14, 16, 18, 20, 21, 22, 24, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "specifi": [2, 4, 5, 8, 13, 21, 24, 27, 29, 30, 31, 33, 36, 37, 38, 41, 44, 45, 47, 49, 57, 65, 67, 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68], "satisfi": [2, 3, 9, 15, 16, 18, 19, 20, 27, 29, 30, 31, 32, 33, 38, 41, 45, 46, 48, 56, 58, 61, 64], "custom": [2, 20, 21, 29, 32, 36, 38, 41, 50, 56, 61, 68], "restrict": [2, 16, 25, 30, 34, 45, 48, 52, 54, 67, 68], "possibl": [2, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 24, 27, 29, 30, 32, 33, 34, 37, 38, 39, 41, 46, 49, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 67, 68], "condit": [2, 3, 8, 15, 18, 23, 24, 26, 27, 29, 30, 41, 43, 49, 54, 58, 63, 67, 68], "must": [2, 3, 5, 19, 21, 23, 26, 27, 29, 31, 32, 34, 37, 38, 52, 62, 63, 64, 65, 67], "requir": [2, 3, 4, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 38, 41, 44, 46, 49, 51, 52, 56, 58, 61, 62, 63, 65, 67, 68], "maximum": [2, 3, 8, 11, 15, 18, 23, 24, 25, 29, 33, 34, 38, 43, 46, 47, 48, 52, 54, 56, 62, 65, 67], "allow": [2, 4, 8, 18, 23, 24, 27, 29, 31, 32, 34, 40, 44, 47, 48, 52, 54, 56, 64, 67], "budget": [2, 12, 36, 51, 62], "demand": [2, 3, 4, 6, 18, 20, 26, 30, 32, 33, 34, 36, 38, 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30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "candid": [2, 3, 7, 21, 36], "meet": [2, 3, 5, 12, 18, 22, 25, 29, 30, 32, 34, 36, 38, 42, 51, 56, 58, 64, 65], "best": [2, 5, 7, 8, 29, 30, 34, 43, 56, 62, 65, 68], "global": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 45, 47, 51, 54, 56, 59, 61, 65, 68], "optimum": [2, 40, 43], "among": [2, 3, 7, 11, 14, 15, 21, 22, 26, 31, 32, 34, 46, 47, 48, 61, 63, 64, 68], "appli": [2, 4, 5, 18, 24, 34, 36, 38, 41, 43, 44, 48, 64, 65, 67], "type": [2, 3, 4, 5, 6, 8, 12, 16, 17, 19, 21, 25, 29, 30, 34, 36, 41, 44, 49, 51, 54, 56, 64, 65, 67], "relat": [2, 5, 18, 23, 26, 29, 38, 49, 54, 56, 61, 62, 64], "fundament": [2, 14, 38, 67], "question": [2, 8, 15, 22, 41, 44, 50, 58, 63, 64], "translat": [2, 4, 5, 18, 22, 29, 32, 36, 43], "real": [2, 4, 5, 7, 13, 16, 21, 30, 31, 34, 35, 36, 39, 45, 46, 48, 50, 52, 54, 56, 58, 61, 62, 64], "abstract": [2, 4, 8, 67], "represent": [2, 21, 41, 43, 49], "Not": [2, 29], "aspect": [2, 6, 8, 14, 16, 28, 35, 42, 53, 55, 60], "taken": [2, 29, 62, 64, 68], "account": [2, 10, 18, 23, 24, 29, 31, 34, 42, 45, 46, 54, 58, 59, 62, 64, 65, 67], "so": [2, 3, 4, 5, 8, 9, 13, 18, 21, 23, 24, 25, 27, 29, 30, 34, 35, 36, 37, 38, 39, 41, 43, 47, 49, 50, 52, 54, 56, 61, 62, 65, 67], "mani": [2, 3, 4, 8, 16, 19, 20, 22, 23, 27, 29, 30, 31, 34, 36, 41, 44, 45, 46, 48, 49, 51, 52, 54, 58, 61, 62, 64, 65, 68], "made": [2, 3, 10, 12, 18, 20, 24, 29, 51, 58, 62, 65], "step": [2, 3, 4, 8, 9, 12, 14, 15, 18, 21, 29, 44, 45, 51, 52, 61, 62, 67, 68], "typic": [2, 21, 34, 56, 58, 62, 67, 68], "signific": [2, 5, 8, 40, 44, 45, 46, 48, 49], "impact": [2, 7, 38, 50, 63], "approach": [2, 5, 7, 8, 10, 13, 14, 17, 19, 20, 22, 26, 27, 30, 36, 38, 43, 45, 48, 56, 62, 64, 67], "There": [2, 5, 18, 19, 22, 25, 26, 29, 30, 34, 36, 38, 43, 44, 58, 62, 67, 68], "multipl": [2, 3, 12, 15, 18, 27, 29, 31, 32, 33, 36, 38, 68], "equival": [2, 5, 8, 18, 20, 22, 41, 43, 52, 54, 57, 61, 68], "formul": [2, 3, 4, 6, 8, 9, 11, 13, 14, 15, 16, 18, 22, 23, 26, 27, 28, 29, 30, 32, 34, 35, 38, 39, 43, 48, 50, 54, 56, 58, 59, 61, 64, 65, 68], "conceptu": 2, "same": [2, 3, 4, 5, 10, 11, 13, 14, 15, 18, 19, 21, 22, 25, 27, 29, 30, 31, 32, 33, 39, 41, 44, 45, 49, 52, 54, 56, 59, 62, 64, 65, 67, 68], "comput": [2, 4, 5, 7, 8, 14, 15, 18, 22, 23, 29, 30, 31, 34, 36, 38, 41, 44, 45, 48, 49, 51, 58, 61, 63, 64, 65, 67, 68], "complex": [2, 4, 5, 15, 21, 22, 23, 29, 31, 36, 67, 68], "mai": [2, 3, 4, 5, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23, 27, 29, 31, 32, 34, 36, 38, 40, 41, 44, 45, 49, 51, 54, 56, 57, 63, 67], "differ": [2, 3, 5, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 27, 29, 30, 32, 33, 34, 36, 38, 39, 40, 41, 43, 44, 47, 48, 49, 50, 52, 54, 56, 58, 59, 62, 63, 64, 65, 68], "interpret": [2, 4, 15, 40], "s": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "after": [2, 5, 13, 23, 29, 30, 31, 39, 46, 52, 54, 58, 62, 63, 64, 65, 67], "ha": [2, 3, 4, 5, 7, 10, 12, 14, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67], "back": [2, 5, 13, 23, 29, 31, 41, 49, 62, 67], "origin": [2, 3, 4, 8, 9, 10, 13, 14, 17, 25, 27, 30, 34, 36, 38, 44, 47], "These": [2, 4, 15, 25, 27, 29, 33, 34, 38, 41, 44, 46, 48, 52, 56, 58, 61, 67, 68], "treat": [2, 21, 30, 50, 65], "continu": [2, 12, 18, 22, 23, 38, 41, 43, 49, 51, 59, 65], "process": [2, 4, 23, 29, 38, 40, 41, 44, 49, 52, 54, 67, 68], "sequenti": 2, "final": [2, 4, 5, 12, 14, 21, 22, 23, 29, 38, 44, 61, 64], "turn": [2, 3, 16, 21, 30, 35, 38, 41, 49, 52, 56, 65], "out": [2, 3, 23, 24, 27, 29, 30, 32, 35, 36, 38, 52, 56, 64, 65], "impract": [2, 29], "adjust": [2, 4, 14, 29, 34, 40, 56, 61, 64, 65], "certain": [2, 16, 29, 30, 32, 41, 44, 49, 54, 56, 61, 65], "desir": [2, 3, 12, 18, 25, 30, 35, 51, 67], "properti": [2, 8, 30, 35, 43, 45, 47, 48, 56], "cannot": [2, 8, 12, 16, 21, 22, 25, 29, 34, 43, 51, 52, 59, 61, 62, 65], "effici": [2, 10, 14, 21, 27, 29, 30, 47, 48, 49, 54, 68], "perhap": [2, 19, 32, 36], "re": [2, 68], "includ": [2, 5, 6, 14, 16, 18, 19, 21, 22, 23, 24, 27, 28, 29, 31, 32, 35, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 52, 56, 57, 61, 62, 63, 64, 65, 66, 67, 68], "well": [2, 5, 7, 13, 14, 19, 22, 27, 29, 35, 36, 43, 45, 46, 59, 63, 68], "except": [2, 20, 25, 65, 67, 68], "mathematician": 2, "studi": [2, 15, 21, 27, 36, 38, 43, 56, 59, 68], "alwai": [2, 4, 10, 12, 17, 24, 34, 37, 39, 41, 45, 46, 47, 49, 50, 54, 57, 61, 67], "flaw": 2, "challeng": [2, 8, 19, 27, 31, 36, 38, 41], "follow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "given": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 38, 41, 43, 44, 45, 46, 48, 49, 50, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "f": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "x": [2, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 49, 51, 52, 54, 56, 57, 58, 59, 62, 63, 64, 65, 67, 68], "mathbb": [2, 6, 8, 13, 16, 17, 18, 26, 34, 36, 37, 39, 41, 44, 45, 46, 48, 49, 54, 56, 57, 58, 59, 61, 64, 65], "r": [2, 4, 6, 8, 13, 14, 16, 18, 19, 21, 22, 24, 25, 27, 29, 31, 32, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 57, 58, 59, 61, 62, 63, 64, 65, 67], "subseteq": [2, 16, 34, 52], "n": [2, 6, 7, 8, 9, 13, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 59, 61, 62, 68], "being": [2, 5, 6, 8, 34, 37, 38, 49, 52, 65], "y": [2, 4, 7, 8, 9, 13, 14, 18, 19, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 43, 44, 46, 47, 48, 49, 54, 56, 58, 59, 62, 63, 65, 67, 68], "geq": [2, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 59, 61, 62, 64, 65, 67], "foral": [2, 4, 7, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 25, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 44, 45, 47, 48, 49, 52, 54, 56, 58, 61, 62, 64, 65], "least": [2, 6, 7, 12, 18, 22, 24, 34, 35, 36, 38, 51, 52, 62, 63], "good": [2, 4, 5, 7, 15, 20, 27, 29, 33, 37, 45, 48, 58, 62, 63, 68], "similarli": [2, 8, 13, 18, 34, 39], "chang": [2, 4, 5, 10, 14, 15, 21, 23, 24, 27, 29, 30, 32, 34, 37, 38, 39, 40, 47, 50, 52, 54, 56, 57, 61, 62, 64, 67, 68], "last": [2, 12, 18, 22, 23, 24, 29, 34, 37, 38, 51, 52, 54, 63, 65], "leq": [2, 3, 4, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 61, 62, 64, 65, 68], "case": [2, 3, 4, 5, 7, 8, 13, 16, 20, 21, 25, 26, 27, 29, 30, 32, 36, 39, 41, 45, 46, 48, 49, 54, 56, 58, 59, 65, 67, 68], "refer": [2, 3, 4, 6, 8, 13, 16, 20, 24, 27, 29, 32, 33, 38, 39, 41, 46, 49, 64, 68], "begin": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "align": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "min": [2, 6, 7, 8, 9, 11, 13, 14, 16, 18, 20, 22, 24, 25, 27, 29, 30, 32, 33, 34, 36, 38, 39, 41, 43, 44, 45, 49, 52, 54, 56, 58, 59, 62, 64, 65, 68], "quad": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 37, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 57, 58, 59, 61, 62, 64, 67, 68], "text": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "t": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "end": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "lead": [2, 14, 17, 18, 19, 20, 21, 22, 26, 29, 35, 38, 41, 43, 49, 54, 56, 62, 65, 68], "techniqu": [2, 7, 11, 13, 21, 22, 23, 26, 32, 41, 44, 48, 56, 62, 64], "introductori": [2, 60], "present": [2, 4, 5, 7, 8, 13, 14, 24, 29, 31, 32, 34, 36, 38, 39, 40, 41, 45, 46, 47, 48, 49, 52, 61, 63, 65, 67, 68], "simpl": [2, 3, 4, 5, 12, 21, 26, 30, 31, 35, 36, 38, 40, 41, 44, 49, 54, 67, 68], "context": [2, 15, 25, 33, 57, 62, 65], "plan": [2, 4, 5, 10, 11, 15, 16, 18, 24, 25, 27, 38, 44, 50, 58, 60], "explain": [2, 7, 34, 38, 49, 56, 65], "structur": [2, 4, 8, 17, 26, 28, 30, 36, 43, 61, 62, 64, 65, 67, 68], "formal": [2, 16], "implement": [2, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 22, 24, 27, 28, 29, 32, 34, 35, 36, 37, 38, 42, 43, 44, 45, 50, 53, 55, 56, 57, 58, 60, 61, 62, 67, 68], "second": [2, 4, 5, 10, 13, 18, 21, 27, 29, 31, 32, 34, 36, 37, 38, 41, 49, 59, 60, 61, 62, 63, 64, 65], "also": [2, 3, 4, 5, 8, 11, 12, 14, 15, 18, 20, 22, 23, 25, 29, 30, 34, 35, 37, 38, 40, 41, 44, 52, 54, 57, 61, 62, 64, 65, 68], "serv": [2, 4, 5, 20, 33, 52, 62], "tutori": [2, 25, 36, 45, 46, 68], "lastli": [2, 3, 61, 62, 65], "discuss": [2, 32, 38, 41, 44, 49, 50, 62], "some": [2, 3, 4, 5, 7, 8, 13, 16, 21, 23, 27, 29, 30, 32, 34, 38, 39, 40, 41, 43, 45, 46, 48, 49, 54, 56, 62, 64, 65, 67, 68], "advanc": [2, 3, 5, 23, 38, 44, 50, 62, 64, 65], "featur": [2, 4, 5, 6, 8, 13, 16, 20, 21, 22, 23, 27, 33, 38, 39, 46, 48, 59, 61, 67, 68], "third": [2, 4, 5, 32], "go": [2, 6, 8, 13, 14, 16, 21, 28, 30, 34, 35, 38, 39, 42, 50, 52, 53, 55, 62, 63, 64, 67], "next": [2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 21, 22, 23, 25, 28, 29, 30, 32, 33, 35, 37, 42, 45, 49, 50, 52, 53, 55, 56, 59, 63, 64, 65], "about": [2, 6, 8, 16, 20, 24, 28, 29, 30, 35, 38, 42, 50, 53, 55, 56, 58, 61, 62, 64], "linear": [2, 3, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 19, 20, 22, 23, 26, 29, 31, 34, 35, 38, 39, 40, 54, 56, 59, 60, 61, 62, 64], "compani": [3, 6, 8, 12, 17, 18, 33, 38, 44, 50, 51, 52, 54, 62], "produc": [3, 4, 7, 8, 10, 12, 15, 17, 21, 23, 26, 29, 31, 32, 34, 36, 38, 41, 45, 46, 51, 52, 54, 56, 62, 64, 65, 67], "two": [3, 4, 5, 6, 8, 12, 13, 14, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 32, 36, 38, 41, 44, 46, 49, 52, 54, 55, 56, 58, 59, 64, 67, 68], "version": [3, 4, 5, 10, 18, 20, 29, 30, 34, 38, 44, 52, 53, 54, 62, 64, 65, 68], "each": [3, 4, 5, 8, 9, 12, 13, 14, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 41, 45, 46, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "raw": [3, 5, 8, 12, 18, 26, 29, 34, 38, 41, 49, 51, 56, 62, 64, 65], "10": [3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 38, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "per": [3, 8, 10, 12, 14, 18, 23, 24, 25, 26, 29, 32, 33, 36, 43, 51, 59, 62, 63, 64], "gram": [3, 18, 41, 49], "special": [3, 4, 11, 12, 13, 16, 28, 30, 36, 39, 44, 61, 68], "labor": [3, 4, 26, 62, 67], "finish": [3, 12, 18, 21, 22, 23, 26, 36, 51, 52, 68], "u": [3, 4, 5, 14, 18, 23, 29, 33, 41, 43, 44, 45, 48, 49, 52, 54, 56, 62, 67, 68], "higher": [3, 14, 26, 29, 38, 41, 46, 49, 51, 56, 58, 62, 64], "270": [3, 4, 5, 26, 29, 62, 65, 67], "unit": [3, 4, 5, 10, 12, 14, 15, 18, 23, 24, 26, 29, 31, 32, 33, 34, 38, 43, 44, 46, 51, 56, 57, 58, 59, 61, 62, 63, 64, 67], "one": [3, 4, 5, 6, 8, 9, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 61, 62, 63, 64, 65], "hour": [3, 4, 21, 24, 25, 26, 52, 62], "b": [3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 19, 21, 22, 25, 26, 29, 32, 33, 34, 36, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 52, 59, 61, 62, 64, 65, 67, 68], "due": [3, 4, 12, 14, 18, 29, 34, 37, 38, 50, 51, 54, 58, 61, 63, 64, 68], "market": [3, 4, 14, 15, 29, 31, 38, 45, 46, 48, 61, 62, 63], "limit": [3, 5, 12, 18, 20, 21, 24, 25, 26, 29, 32, 34, 36, 38, 40, 43, 45, 48, 51, 56, 62, 64, 65, 68], "40": [3, 4, 5, 7, 24, 25, 26, 29, 32, 36, 43, 44, 45, 58, 61, 62, 63, 67, 68], "week": [3, 25, 61], "v": [3, 4, 5, 14, 20, 21, 24, 29, 30, 33, 34, 38, 41, 44, 45, 49, 52, 56, 62, 64, 65, 67, 68], "lower": [3, 4, 5, 12, 14, 21, 29, 31, 32, 36, 38, 39, 44, 54, 58, 61, 63, 64, 68], "unlimit": [3, 4, 26, 36, 38, 62], "210": [3, 4, 5, 26, 61, 62, 63, 67], "9": [3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 44, 50, 51, 52, 54, 61, 62, 65, 67], "1": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 62, 64, 65, 67, 68], "summar": [3, 4, 25, 32, 34, 38, 41, 49, 58, 61, 62, 63], "tabl": [3, 4, 18, 21, 22, 24, 26, 27, 31, 41, 45, 51, 52, 54, 58, 59, 61, 63, 68], "g": [3, 4, 5, 8, 14, 18, 20, 22, 27, 29, 30, 31, 32, 33, 34, 38, 39, 41, 43, 44, 49, 52, 54, 59, 64, 65, 67], "hr": [3, 4, 24, 52], "2": [3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 62, 64, 65, 67, 68], "weekli": [3, 25], "avail": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 44, 45, 47, 48, 51, 52, 54, 56, 58, 59, 61, 62, 65, 67], "inventori": [3, 12, 18, 29, 44, 51, 58, 59], "shelf": 3, "life": [3, 22, 30, 35, 43, 52], "order": [3, 4, 10, 12, 18, 21, 22, 25, 30, 32, 34, 36, 40, 41, 42, 49, 51, 52, 58, 62], "left": [3, 7, 8, 9, 10, 12, 13, 21, 22, 23, 24, 25, 27, 30, 32, 36, 38, 39, 41, 43, 44, 46, 48, 49, 51, 52, 56, 59, 61, 62, 64], "over": [3, 4, 10, 11, 14, 18, 21, 23, 25, 29, 30, 34, 36, 38, 40, 41, 44, 46, 49, 51, 58, 61, 62, 63, 67, 68], "discard": 3, "detail": [3, 8, 20, 25, 41, 43, 49, 58], "resourc": [3, 4, 15, 21, 25, 27, 29, 30, 36, 51, 63, 64, 67], "amount": [3, 4, 12, 15, 16, 17, 18, 21, 22, 25, 29, 31, 32, 33, 37, 38, 50, 51, 54, 56, 57, 59, 62, 63, 64, 68], "80": [3, 4, 5, 26, 29, 32, 34, 40, 46, 62, 63, 65, 67], "50": [3, 4, 5, 9, 14, 18, 24, 26, 29, 31, 32, 34, 36, 38, 41, 43, 44, 48, 51, 54, 59, 61, 62, 63, 65, 67, 68], "100": [3, 4, 5, 10, 18, 19, 20, 22, 24, 26, 29, 31, 32, 34, 36, 38, 40, 41, 44, 46, 48, 49, 51, 54, 56, 59, 61, 62, 63, 64, 65, 67, 68], "want": [3, 14, 17, 18, 20, 29, 32, 37, 39, 41, 47, 54, 57, 61, 62, 68], "its": [3, 5, 7, 8, 9, 13, 14, 20, 22, 30, 33, 39, 41, 44, 49, 54, 62, 64, 67, 68], "gross": [3, 45, 46, 48], "profit": [3, 4, 5, 6, 8, 9, 10, 17, 23, 26, 29, 32, 36, 38, 40, 54, 61, 62, 63, 67, 68], "much": [3, 8, 12, 13, 17, 18, 23, 24, 27, 30, 36, 43, 46, 50, 62, 64, 65], "abov": [3, 4, 6, 8, 11, 12, 13, 14, 17, 18, 22, 23, 24, 29, 30, 32, 34, 36, 38, 39, 43, 44, 45, 47, 48, 49, 51, 54, 56, 59, 61, 62, 64, 68], "optim": [3, 4, 5, 9, 10, 11, 13, 17, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 39, 40, 44, 45, 51, 52, 54, 56, 58, 60, 66, 68], "reformul": [3, 5, 10, 13, 14, 43, 45, 46, 48, 49, 54, 61], "involv": [3, 5, 6, 8, 11, 21, 31, 38, 41, 44, 45, 46, 48], "few": [3, 4, 5, 32, 41, 61, 67], "crucial": [3, 34], "element": [3, 4, 5, 8, 25, 30, 32, 41, 43, 46, 49, 64, 67, 68], "start": [3, 5, 7, 8, 13, 14, 18, 21, 22, 23, 24, 27, 29, 31, 36, 48, 54, 61, 62], "point": [3, 4, 7, 8, 13, 16, 20, 23, 27, 29, 33, 38, 39, 40, 41, 44, 46, 49, 54, 59, 61, 65, 68], "list": [3, 4, 8, 14, 19, 21, 24, 25, 26, 29, 30, 31, 34, 36, 41, 43, 45, 48, 49, 51, 52, 56, 59, 61, 62, 64, 67, 68], "relev": [3, 4, 18, 20, 27, 29, 65, 67, 68], "hand": [3, 4, 8, 9, 17, 20, 24, 25, 29, 30, 31, 41, 49, 54, 56, 58], "quantiti": [3, 8, 11, 12, 18, 33, 34, 36, 38, 42, 50, 51, 54, 62], "modifi": [3, 5, 17, 20, 21, 23, 29, 31, 41, 54, 56, 61, 64, 65, 67, 68], "achiev": [3, 11, 14, 23, 25, 37, 41, 49, 64, 67], "outcom": [3, 15, 32, 41, 45, 49, 56, 58, 62], "while": [3, 5, 8, 9, 12, 14, 17, 18, 20, 21, 24, 25, 26, 29, 33, 34, 36, 41, 47, 48, 49, 50, 51, 52, 54, 56, 58, 61, 62, 64, 65], "stage": [3, 5, 27, 38, 45, 46, 59, 63, 64, 67, 68], "prove": [3, 8], "redund": [3, 68], "later": [3, 4, 5, 15, 21, 24, 27, 44, 46, 65, 68], "creat": [3, 12, 13, 14, 15, 19, 21, 22, 23, 27, 29, 30, 31, 34, 36, 38, 41, 44, 45, 48, 49, 56, 58, 61, 62, 63, 67, 68], "comprehens": [3, 7, 41, 49], "below": [3, 5, 9, 11, 12, 14, 17, 20, 21, 24, 29, 30, 34, 36, 38, 41, 43, 44, 45, 49, 54, 56, 59, 61, 62, 65, 68], "symbol": [3, 14, 22, 29, 31, 32, 48, 58, 67, 68], "descript": [3, 15, 21, 32, 34, 41, 50, 54, 65], "upper": [3, 4, 5, 9, 12, 14, 21, 22, 23, 24, 29, 32, 36, 37, 38, 39, 43, 51, 54, 59, 67, 68], "bound": [3, 4, 5, 9, 14, 19, 21, 22, 23, 24, 26, 29, 30, 31, 36, 37, 38, 41, 43, 52, 54, 56, 62, 63, 65, 67], "known": [3, 4, 5, 7, 13, 14, 19, 21, 22, 23, 27, 37, 38, 39, 41, 43, 44, 46, 48, 49, 52, 56, 58, 62, 63, 64, 65], "x_m": [3, 5, 59], "0": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "x_a": [3, 5], "x_b": [3, 5], "y_u": [3, 5], "y_v": [3, 5], "measur": [3, 7, 10, 14, 23, 24, 25, 29, 38, 41, 49, 50], "max": [3, 4, 7, 8, 9, 10, 11, 14, 15, 17, 21, 22, 23, 24, 25, 26, 27, 29, 34, 36, 37, 38, 40, 41, 43, 45, 46, 47, 49, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "equal": [3, 5, 6, 7, 8, 10, 12, 15, 16, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 43, 44, 45, 46, 48, 52, 54, 56, 58, 59, 61, 62, 64, 65], "between": [3, 4, 5, 8, 10, 13, 14, 20, 21, 24, 25, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 43, 45, 46, 47, 49, 52, 56, 63, 64, 65, 67, 68], "revenu": [3, 4, 5, 10, 11, 40, 58, 61, 63, 67], "written": [3, 4, 5, 15, 41, 44, 57, 58, 61, 67, 68], "As": [3, 4, 5, 8, 14, 18, 21, 25, 29, 30, 31, 38, 41, 44, 45, 49, 52, 58, 59, 62, 65, 68], "shown": [3, 5, 38, 41, 44, 45, 68], "here": [3, 4, 5, 10, 12, 13, 21, 22, 24, 25, 26, 29, 31, 38, 39, 41, 44, 45, 46, 48, 51, 52, 54, 56, 57, 58, 61, 62, 65, 67, 68], "algebra": [3, 5, 19, 40, 41, 44], "combin": [3, 4, 5, 18, 19, 29, 38, 43, 52, 56, 62, 64, 67, 68], "name": [3, 4, 5, 9, 10, 12, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 29, 30, 32, 34, 36, 39, 40, 41, 47, 49, 58, 59, 61, 64, 65], "when": [3, 4, 5, 7, 8, 12, 13, 16, 20, 21, 22, 23, 24, 25, 29, 31, 32, 34, 38, 40, 41, 42, 44, 45, 46, 49, 56, 58, 59, 62, 63, 64, 65, 66, 67], "place": [3, 4, 29, 30, 31, 34, 38, 44, 45, 58, 67, 68], "break": [3, 4, 29, 52, 56, 62, 65], "up": [3, 4, 5, 12, 16, 17, 18, 19, 21, 23, 29, 36, 41, 45, 46, 49, 51, 55, 56, 61, 62, 63], "longer": [3, 20, 21, 30, 44, 49, 52, 56, 61, 67], "smaller": [3, 4, 10, 16, 27, 50, 56], "simplifi": [3, 5, 24, 27, 49, 61, 67, 68], "relationship": [3, 5, 13, 22, 24, 25, 27, 29, 31, 43, 44, 45, 68], "inequ": [3, 6, 8, 9, 38, 46, 54, 64], "overal": [3, 8, 15, 25, 38, 43], "now": [3, 4, 5, 7, 8, 12, 14, 18, 20, 21, 24, 25, 27, 29, 30, 31, 32, 33, 34, 36, 41, 44, 45, 46, 47, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67], "readi": [3, 29, 31, 44, 49, 62], "full": [3, 7, 13, 20, 23, 24, 32, 36, 38, 39, 41, 43, 48, 49, 54, 61, 62, 68], "canon": [3, 6, 37], "nonumb": [3, 4, 30], "textbook": [3, 20, 62, 63], "customari": [3, 8, 49], "under": [3, 14, 15, 18, 20, 25, 26, 51, 56, 58, 61, 62, 63, 67], "clearli": [3, 4, 7, 18, 20, 23, 25, 38, 52, 62, 65], "distinguish": [3, 29, 65, 68], "paramet": [3, 4, 5, 18, 21, 23, 29, 32, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 54, 56, 58, 59, 61, 62, 64, 65, 67], "might": [3, 15, 16, 27, 29, 31, 34, 35, 38, 41, 50, 58, 64, 67], "letter": [3, 19, 44, 62], "throughout": [3, 8, 29, 62, 68], "websit": 3, "howev": [3, 4, 8, 13, 15, 16, 17, 18, 20, 28, 29, 30, 31, 34, 35, 38, 41, 43, 44, 47, 48, 49, 52, 54, 56, 58, 61, 62, 64, 65, 68], "stick": 3, "convent": [3, 5, 6, 8, 56, 64, 65], "onli": [3, 4, 7, 8, 9, 16, 18, 20, 21, 24, 25, 26, 27, 29, 30, 32, 34, 36, 38, 41, 48, 52, 54, 56, 58, 61, 62, 63, 64, 68], "explicitli": [3, 8, 17, 29, 30, 31, 49, 52, 61, 63, 68], "domain": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67], "vector": [3, 6, 8, 11, 12, 13, 34, 35, 37, 39, 40, 42, 57, 61, 62], "minimum": [3, 11, 12, 14, 16, 21, 24, 25, 28, 29, 34, 36, 38, 43, 52, 61, 63], "even": [3, 5, 20, 25, 29, 30, 38, 41, 47, 49, 52, 61, 62, 64, 65], "like": [3, 4, 8, 9, 11, 12, 14, 18, 29, 30, 31, 32, 36, 38, 41, 49, 51, 61, 62, 64, 65], "immedi": [3, 21, 29], "clear": [3, 9, 17, 35, 56, 62, 68], "what": [3, 8, 15, 21, 22, 25, 29, 31, 38, 40, 42, 45, 46, 50, 54, 56, 58, 61, 62, 63, 65, 67], "exactli": [3, 14, 33, 34, 44, 49, 50, 52, 54, 61, 62, 65], "where": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 40, 41, 43, 44, 45, 46, 47, 48, 49, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67, 68], "come": [3, 4, 14, 15, 30, 35, 41, 49, 62], "plai": [3, 8, 35, 45, 49, 52, 62], "thei": [3, 5, 8, 16, 18, 22, 24, 29, 30, 31, 32, 35, 36, 38, 41, 48, 50, 52, 56, 61, 62, 64, 65, 67, 68], "procedur": [3, 36, 54, 62], "long": [3, 4, 14, 16, 22, 25, 29, 45, 46, 59, 62, 68], "standard": [3, 5, 6, 8, 13, 14, 22, 29, 34, 38, 40, 41, 49, 57, 61, 68], "fashion": [3, 4, 52], "practition": [3, 48], "often": [3, 4, 16, 21, 28, 32, 34, 50, 62, 65, 68], "boil": 3, "down": [3, 17, 23, 29, 30, 34, 46, 61, 62, 63], "pass": [3, 4, 5, 18, 20, 21, 24, 28, 41, 49, 67], "softwar": [3, 26], "regardless": [3, 4, 8, 12, 18, 29, 45, 51, 59], "wa": [3, 7, 14, 25, 29, 30, 32, 34, 35, 36, 38, 44, 45, 46, 47, 48, 52, 54, 58, 62, 64], "stori": [3, 45, 46], "behind": 3, "To": [3, 5, 8, 18, 21, 22, 23, 24, 25, 27, 29, 30, 34, 36, 38, 41, 43, 48, 49, 52, 54, 56, 58, 59, 61, 62, 64, 65, 67], "do": [3, 4, 8, 20, 23, 25, 29, 30, 31, 34, 38, 40, 44, 52, 54, 56, 61, 62, 64, 65, 67, 68], "interfac": [3, 4, 5, 14, 44, 68], "commun": [3, 5, 7, 20, 25, 27, 63], "adopt": [3, 8, 26, 32, 41, 43, 46, 54], "python": [3, 5, 8, 13, 17, 20, 21, 25, 29, 30, 32, 36, 39, 40, 41, 44, 49, 52, 56, 65, 67, 68], "base": [3, 5, 7, 12, 13, 19, 21, 26, 29, 32, 34, 39, 40, 41, 46, 48, 49, 50, 51, 56, 62, 64, 67, 68], "carri": [3, 24, 34], "ll": [4, 31, 38], "revisit": [4, 16, 25, 42, 60], "time": [4, 5, 6, 8, 14, 18, 20, 23, 24, 25, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 48, 49, 50, 51, 52, 56, 58, 61, 62, 64, 65, 67, 68], "capabl": [4, 7, 26, 38, 54], "scale": [4, 7, 29, 30, 34, 36, 38, 41, 56, 59, 61, 62, 65], "enabl": [4, 5, 28, 29, 44, 49, 67], "vari": [4, 12, 37, 49, 51, 54, 56, 63], "addit": [4, 6, 7, 12, 15, 18, 21, 23, 25, 27, 29, 36, 41, 44, 46, 48, 49, 58, 61, 62, 65, 67, 68], "compon": [4, 5, 8, 13, 14, 18, 22, 26, 37, 44, 49, 57, 68], "index": [4, 5, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 38, 39, 40, 41, 43, 44, 48, 49, 51, 52, 56, 58, 61, 63, 64, 67], "essenti": [4, 22, 29, 30, 36, 41, 49, 52, 54, 62], "scalabl": [4, 6], "maintain": [4, 5, 8, 27, 29, 44, 65, 68], "more": [4, 5, 8, 10, 11, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 34, 36, 37, 38, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 63, 64, 65, 67, 68], "analysi": [4, 7, 8, 12, 13, 21, 29, 32, 38, 41, 45, 46, 49, 50, 52, 54, 61, 68], "examin": [4, 7, 17, 38, 48], "identifi": [4, 5, 16, 18, 24, 27, 29, 31, 33, 36, 38, 41, 43, 46, 49, 61, 65, 68], "underli": [4, 39, 62], "verifi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 49, 51, 52, 54, 56, 58, 59, 61, 65], "high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 38, 41, 43, 48, 49, 51, 54, 56, 58, 59, 62, 64, 65, 68], "via": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 49, 50, 54, 56, 59, 65], "appsi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 51, 54, 59, 65], "modul": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "elsewher": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 37, 47, 51, 54, 56, 59, 65, 68], "assum": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 41, 43, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68], "cbc": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 38, 40, 51, 52, 54, 58, 59, 61, 62, 63, 64, 65, 67], "been": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 41, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 59, 61, 63, 65, 67, 68], "previous": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 37, 46, 47, 49, 51, 52, 54, 56, 59, 61, 64, 65, 67, 68], "solverfactori": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "It": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 44, 47, 51, 54, 56, 59, 62, 64, 65], "sy": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "pip": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67], "dev": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65], "null": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65], "highspi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 40, 51, 52, 54, 58, 59, 61, 62, 63, 64, 65], "environ": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "appsi_high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 40, 52, 54, 58, 59, 61, 62, 63, 64, 65], "els": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 31, 33, 34, 37, 41, 44, 46, 47, 49, 51, 52, 54, 59, 61, 62, 64, 65, 67, 68], "assert": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 37, 47, 51, 54, 56, 59], "basic": [4, 16, 17, 21, 27, 41, 54, 64, 67, 68], "previou": [4, 9, 12, 15, 18, 28, 29, 34, 41, 44, 49, 50, 62], "were": [4, 18, 27, 30, 34, 41, 48, 61, 64], "distinct": [4, 22, 29], "evid": [4, 20], "compris": [4, 21, 23, 25, 31, 36, 39, 58, 67, 68], "abbrevi": [4, 29, 67], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "factor": [4, 7, 20, 41, 43, 44, 47, 49, 54, 61, 64, 68], "attribut": [4, 5, 7, 14, 29, 34, 36, 44, 64, 67, 68], "built": [4, 20, 56], "librari": [4, 5, 8, 13, 21, 29, 30, 32, 39, 40, 41, 43, 44, 46, 48, 49, 68], "handl": [4, 23, 26, 44, 67], "tabular": [4, 5], "sever": [4, 5, 6, 7, 11, 16, 19, 24, 26, 28, 29, 35, 36, 38, 41, 48, 56, 68], "option": [4, 5, 14, 21, 27, 29, 40, 41, 49, 62, 64, 67, 68], "would": [4, 7, 8, 12, 15, 17, 18, 19, 21, 25, 26, 29, 31, 32, 34, 38, 41, 44, 47, 48, 49, 51, 52, 54, 56, 58, 61, 62, 63, 64, 65, 67], "appropri": [4, 5, 41, 49], "task": [4, 16, 20, 24, 29, 30, 32, 36, 41, 52], "nest": [4, 36, 62, 67, 68], "dictionari": [4, 18, 21, 29, 33, 34, 36, 41, 44, 49, 52, 56, 62, 68], "column": [4, 5, 7, 12, 14, 15, 18, 20, 22, 24, 25, 29, 30, 31, 32, 34, 38, 41, 44, 48, 49, 51, 52, 61, 63, 65, 68], "show": [4, 5, 7, 9, 12, 13, 14, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 43, 44, 46, 47, 50, 51, 52, 54, 56, 58, 59, 61, 62, 65, 67, 68], "repres": [4, 6, 8, 18, 19, 22, 23, 25, 31, 33, 34, 36, 38, 41, 43, 44, 45, 46, 48, 49, 56, 58, 67, 68], "kei": [4, 5, 8, 14, 20, 21, 29, 32, 33, 34, 36, 39, 45, 48, 51, 52, 56, 58, 59, 62, 64, 65, 67, 68], "outermost": 4, "inner": [4, 41, 49], "float": [4, 30, 41, 49, 51, 59, 68], "none": [4, 5, 7, 20, 21, 27, 29, 30, 32, 33, 34, 39, 41, 44, 46, 49, 51, 56, 65, 67], 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46, 49, 51, 54, 56, 57, 58, 61, 62, 64, 67], "cal": [4, 49], "p": [4, 11, 12, 18, 23, 24, 26, 34, 36, 38, 40, 41, 43, 45, 46, 48, 49, 51, 52, 56, 57, 59, 61, 64, 65, 67], "respect": [4, 5, 8, 14, 18, 21, 26, 27, 29, 30, 32, 33, 34, 36, 38, 41, 45, 54, 56, 61, 62, 63, 65, 67], "x_r": [4, 38], "y_p": 4, "denot": [4, 8, 12, 15, 18, 19, 21, 23, 26, 27, 29, 30, 31, 34, 36, 38, 39, 48, 52, 56, 61, 64, 68], "constrain": [4, 12, 15, 16, 29, 34, 38, 44, 45, 48], "zero": [4, 13, 19, 29, 34, 36, 39, 40, 41, 43, 44, 49, 51, 59, 62, 64, 65], "b_r": 4, "b_p": 4, "b_q": 4, "don": [4, 8, 29, 56, 65, 68], "insert": [4, 5, 32, 44, 46], "larger": [4, 8, 20, 27, 30, 36, 41, 46, 49, 52, 58], "than": [4, 5, 8, 10, 12, 15, 16, 17, 20, 21, 23, 24, 25, 27, 29, 30, 32, 34, 36, 38, 41, 45, 46, 48, 49, 51, 52, 54, 56, 58, 59, 61, 62, 64], "ever": [4, 41, 49], "design": [4, 5, 14, 19, 22, 27, 32, 38, 39, 41, 42, 65, 67], "caus": [4, 5, 38, 67], "ignor": [4, 34, 41, 49, 56], "befor": [4, 13, 20, 21, 22, 30, 38, 39, 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29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "coeffici": [4, 6, 7, 8, 13, 15, 17, 18, 29, 39, 41, 43, 49, 54, 61, 62], "cp": [4, 40], "cr": 4, "argumentn": 4, "var": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 47, 51, 52, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 67, 68], "fix": [4, 5, 6, 7, 12, 14, 24, 25, 27, 30, 32, 34, 38, 39, 43, 44, 45, 46, 48, 56, 59, 61, 62, 63, 64, 65, 67], "done": [4, 5, 21, 22, 29, 38, 41, 58, 62], "parlanc": 4, "sometim": [4, 35, 45, 57], "call": [4, 8, 24, 27, 29, 31, 32, 34, 37, 38, 39, 41, 47, 48, 49, 52, 58, 61, 65, 67], "rule": [4, 12, 29, 38, 44, 59, 60, 62, 64, 68], "lambda": [4, 18, 19, 20, 21, 22, 25, 27, 29, 31, 34, 38, 41, 43, 45, 46, 48, 49, 51, 52, 54, 56, 59, 64, 65, 67, 68], "argument": [4, 5, 18, 41, 49, 54, 61, 62, 64, 67, 68], "member": [4, 30, 32], "tupl": [4, 5, 8, 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51, 52, 54, 56, 59, 61, 62, 64, 65, 67], "stale": [4, 5, 32, 39], "activ": [4, 5, 25, 26, 29, 32, 34, 54, 65], "bodi": [4, 5, 32, 49], "inf": [4, 5, 62], "12": [4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 34, 36, 37, 41, 46, 47, 48, 52, 54, 58, 61, 62, 68], "2600": [4, 5, 26, 62], "20": [4, 5, 7, 14, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 36, 37, 43, 44, 45, 47, 48, 52, 54, 56, 61, 62, 63, 65, 67, 68], "60": [4, 5, 7, 24, 26, 29, 34, 58, 63, 64, 65, 67], "740": [4, 5, 54, 62, 67], "experienc": [4, 7], "class": [4, 5, 6, 8, 11, 22, 25, 27, 28, 30, 41, 49, 61, 67, 68], "extend": [4, 14, 27, 38, 41, 45, 48, 65, 67, 68], "productionmodel": 4, "inherit": 4, "method": [4, 5, 13, 14, 22, 23, 26, 28, 29, 36, 41, 43, 49, 62, 64, 67, 68], "displai": [4, 5, 7, 8, 12, 13, 14, 15, 18, 22, 24, 25, 27, 29, 30, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 47, 49, 51, 52, 57, 58, 61, 62, 63, 64, 67], "__init__": [4, 34, 41, 49, 67], "self": [4, 34, 41, 49, 67], "instanc": [4, 5, 8, 12, 20, 30, 33, 36, 41, 46, 49, 56, 58, 65, 67, 68], "contain": [4, 5, 13, 20, 25, 26, 29, 34, 36, 39, 41, 49, 54, 56, 65, 67, 68], "inform": [4, 14, 15, 20, 21, 22, 25, 29, 32, 36, 46, 51, 56, 62, 63], "super": 4, "save": [4, 29, 32, 43, 54], "flag": [4, 21, 62, 67], "monitor": [4, 29], "statu": [4, 20, 30, 37, 47, 54, 57, 59, 67], "build_model": [4, 68], "buildth": 4, "integr": [5, 17, 40, 52, 66, 67, 68], "program": [5, 7, 15, 19, 23, 27, 36, 42, 44, 45, 48, 49, 61, 63, 66, 68], "user": [5, 30, 31, 34, 40, 41, 49, 63, 68], "varieti": [5, 34], "project": [5, 38, 41, 44, 49, 66, 68], "ti": 5, "vendor": 5, "constantli": 5, "evolv": 5, "through": [5, 13, 21, 29, 32, 33, 34, 41, 43, 52, 56, 64, 67], "contribut": [5, 7, 14, 19], "parti": [5, 29, 32], "direct": [5, 9, 13, 27, 31, 32, 33, 34, 36, 44, 52, 54], "simplic": [5, 34], "reus": [5, 18, 21], "anoth": [5, 11, 12, 16, 18, 19, 21, 22, 29, 31, 37, 40, 41, 49, 52, 54, 57, 65], "write": [5, 21, 29, 31, 32, 34, 38, 54, 61, 66, 68], "driven": [5, 50], "unfamiliar": [5, 68], "current": [5, 8, 15, 24, 26, 29, 32, 38, 45, 50, 54, 62, 65, 67], "offer": [5, 8, 18, 20, 29, 31, 32, 45, 46, 56, 62, 68], "readabl": [5, 8, 25, 32], "rel": [5, 8, 24, 25, 26, 29, 31, 47, 62, 67], "recent": [5, 29, 34, 36, 38, 43, 45, 46, 48, 68], "intend": [5, 25, 68], "cloud": 5, "person": [5, 7, 21, 44], "session": [5, 20], "mix": [5, 6, 17, 23, 26, 35, 36, 38, 54, 56], "integ": [5, 6, 7, 12, 17, 18, 19, 20, 23, 26, 30, 35, 36, 38, 56, 58, 61, 68], "coin": 5, "OR": [5, 21, 23, 27, 44], "branch": 5, "cut": [5, 20, 23, 26, 27, 35, 44, 56], "suitabl": [5, 22, 41, 49, 67], "glpk": 5, "cplex": [5, 20, 54], "gurobi": [5, 20, 30, 44, 54], "mosek": [5, 37, 44, 46, 47, 48, 49, 56], "check": [5, 8, 29, 31, 37, 38, 44, 47, 49, 50, 54, 57, 64, 65, 67, 68], "quiet": 5, "test": [5, 19, 21, 29, 34, 36, 38, 41, 48, 49, 67, 68], "store": [5, 18, 21, 22, 29, 30, 32, 34, 41, 44, 49, 51, 58, 59, 62, 64, 65, 67, 68], "commonli": [5, 14, 26, 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63], "enough": [7, 8, 17, 25, 26, 29, 35, 38, 58], "replac": [7, 20, 29, 30, 31, 38, 41, 45, 49, 56, 61], "human": 7, "judgment": 7, "judg": 7, "intern": [8, 12, 18, 43, 45, 48, 51, 68], "chip": [8, 10, 12, 17, 18, 51, 54], "1g": 8, "silicon": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "plastic": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "4g": 8, "copper": [8, 9, 10, 11, 12, 15, 17, 18, 51], "memori": [8, 12, 14, 17, 18, 51, 54, 68], "germanium": [8, 9, 10, 11, 12, 15, 17, 18, 51, 54], "2g": 8, "sold": [8, 29, 38, 58, 59, 61, 63], "stock": [8, 12, 18, 35, 38, 45, 46, 48, 50, 51, 55], "1000g": 8, "1500g": 8, "1750g": 8, "4800g": 8, "euro": [8, 31, 63], "12x": 8, "9y": 8, "know": [8, 29, 30, 38, 44, 49, 50, 54, 58, 61, 65], "joint": 8, "usag": [8, 68], "4x": 8, "exce": [8, 25, 30, 38, 65], "1000": [8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 20, 26, 29, 34, 44, 45, 51, 54, 56, 59, 61, 62, 63, 65], "deduc": 8, "role": [8, 29, 35, 38, 52, 54, 62], "1500": [8, 9, 10, 11, 12, 15, 17, 18, 29, 51, 54, 59, 63], "1750": [8, 9, 10, 11, 12, 15, 17, 18, 29, 32, 51, 54], "2y": [8, 68], "4800": [8, 9, 10, 11, 12, 17, 18, 44, 51, 54, 63], "leverag": [8, 30, 31, 44, 48, 54], "dimension": [8, 21, 27, 36, 41, 43, 49, 54, 61], "grai": [8, 24, 33, 34, 52], "enclos": 8, "solid": [8, 33, 64], "isolin": [8, 43], "parallel": 8, "dash": [8, 21, 22], "blue": [8, 13, 21, 34], "increas": [8, 14, 20, 21, 23, 27, 29, 31, 38, 40, 45, 46, 58, 59, 65], "intens": [8, 31, 38], "intuit": [8, 17, 46, 64], "alreadi": [8, 18, 20, 29, 31, 32, 49, 57, 65], "guess": 8, "mark": [8, 24, 29, 45, 49], "easi": [8, 9, 52, 54], "imagin": [8, 29, 30], "complic": [8, 11, 49, 68], "matter": 8, "expand": [8, 62], "obfusc": 8, "discern": 8, "analyz": [8, 28, 61, 65], "matric": [8, 62], "close": [8, 13, 14, 29, 38, 43, 48, 59], "greatli": 8, "identif": [8, 29, 31], "similar": [8, 14, 20, 38, 41, 62, 64, 68], "food": [8, 21, 25, 38], "refresh": 8, "equat": [8, 13, 18, 19, 20, 27, 29, 34, 36, 41, 44, 49, 67], "toward": [8, 25, 27], "renam": 8, "x_1": [8, 9, 10, 17, 37, 41, 43, 47, 49, 54, 57], "x_2": [8, 9, 10, 17, 37, 41, 47, 49, 54, 57], "obtain": [8, 9, 12, 13, 17, 22, 27, 29, 30, 34, 37, 38, 39, 41, 43, 44, 46, 47, 49, 54, 56, 58, 59, 61, 62, 64, 65], "pmatrix": [8, 62], "just": [8, 11, 21, 29, 32, 33, 36, 38, 43, 54, 58, 62, 64], "rewrit": [8, 13, 17, 29, 34, 39, 43, 44, 46, 47, 57, 62], "12x_1": [8, 10, 17, 54], "9x_2": [8, 10, 17, 54], "bmatrix": [8, 19, 22, 24, 26, 27, 41, 44, 49], "system": [8, 18, 29, 31, 34, 38, 45, 46, 55, 62, 65], "read": [8, 14, 21, 23, 29, 34, 36, 43, 48, 56, 61, 62, 65, 67, 68], "correctli": [8, 18, 25, 41, 54, 65], "replic": 8, "4x_1": [8, 10, 54], "longleftrightarrow": [8, 43, 44], "arrai": [8, 13, 34, 37, 39, 41, 43, 44, 47, 48, 49, 54, 56, 57, 61, 62, 64, 65, 68], "2x_2": [8, 10, 54], "x1": [8, 9, 10, 11, 17, 27, 43], "x2": [8, 9, 10, 11, 17, 27], "1f": [8, 10, 11, 15, 21, 41, 44, 45, 49, 59], "650": [8, 10, 54, 58], "1100": [8, 10, 18, 29, 54], "17700": [8, 9, 10, 54], "emploi": [8, 13, 25, 41], "enhanc": [8, 27, 38, 68], "thu": [8, 17, 18, 25, 29, 30, 34, 39, 47, 56, 59, 61, 62, 65], "util": [8, 27, 29, 32, 45, 46, 48, 52, 67], "encompass": 8, "advantag": [8, 18, 28, 30, 31, 41, 49, 67], "concis": [8, 20], "space": [8, 16, 43, 44, 52, 61, 67], "clariti": 8, "reveal": [8, 38, 49], "across": [8, 29, 33, 34, 62, 64], "uniform": [8, 23, 32, 38, 44, 45, 59, 62, 64, 68], "furthermor": [8, 18, 23, 34, 43, 68], "unnecessari": [8, 68], "theoret": [8, 51, 59], "streamlin": [8, 21, 68], "Is": [8, 15, 30, 38, 45], "answer": [8, 41, 58], "textit": 8, "sign": [8, 41, 49], "onc": [8, 22, 23, 36, 41, 49, 61, 65], "commit": [8, 31, 64], "deriv": [8, 22, 27, 34, 40, 41, 44, 49, 61], "sort": [8, 14, 20, 21, 25, 29, 41, 45, 49], "consid": [8, 11, 13, 14, 15, 18, 20, 21, 22, 24, 27, 28, 29, 30, 31, 32, 36, 37, 38, 39, 41, 43, 45, 46, 47, 48, 52, 54, 56, 57, 59, 61, 62, 63, 64, 65, 67], "anywai": [8, 30, 62], "without": [8, 15, 23, 27, 32, 36, 38, 41, 45, 46, 49, 52, 58, 62, 67, 68], "Of": [8, 16, 62], "cours": [8, 16, 17, 22, 38, 62, 68], "illustr": [8, 12, 18, 20, 21, 26, 27, 29, 30, 36, 43, 44, 51, 54, 59, 64, 65], "shall": [8, 18, 29, 43, 44, 49, 64], "upon": [8, 21, 56], "had": [8, 17, 18, 29, 41, 49, 58, 64], "mention": [8, 11], "neg": [8, 9, 17, 18, 21, 29, 33, 34, 36, 41, 44, 46, 49, 50, 56, 58, 61, 68], "therefor": [8, 14, 27, 32, 34, 37, 41, 44, 49, 61, 62, 65], "overlin": 8, "side": [8, 9, 14, 25, 29, 30, 41, 54, 56, 67], "One": [8, 9, 15, 19, 21, 22, 27, 29, 30, 34, 36, 37, 62, 64, 67], "inde": [8, 20, 25, 43, 49, 56, 62, 64], "yield": [8, 10, 17, 18, 27, 29, 38, 40, 44, 49, 51, 52, 54, 56, 62, 63, 67], "n_var": 8, "n_constraint": 8, "constraintlist": [8, 11, 59], "optimal_x": 8, "round": [8, 12, 14, 17, 18, 24, 25, 30, 31, 32, 33, 36, 37, 38, 40, 43, 44, 47, 51, 54, 61, 63, 64], "addition": [8, 57, 62], "avoid": [8, 12, 18, 19, 21, 22, 27, 32, 41, 51, 65, 67, 68], "hard": [8, 27, 29, 41, 49, 62], "doc": [8, 36, 44, 47, 68], "lambda_1": 9, "lambda_2": 9, "lambda_3": 9, "lambda_4": 9, "four": [9, 24, 38, 40, 41, 49, 62], "rh": [9, 49], "tight": [9, 20, 22, 24, 29, 51, 57], "Such": [9, 54, 56, 64, 65], "tightest": 9, "y1": [9, 10, 27, 62], "y2": [9, 10, 27, 62], "y3": [9, 62], "y4": 9, "dualiti": [9, 54], "primal": 9, "coincid": [9, 30], "ask": [9, 29, 31, 63], "suffix": [9, 32, 36, 54, 68], "component_object": [9, 54], "y_": [9, 18, 20, 23, 27, 38, 41, 49, 52, 58, 61], "y_1": [9, 10, 41, 49, 54, 62], "y_2": [9, 10, 41, 49, 54, 62], "y_3": [9, 62], "y_4": 9, "recal": [10, 12, 18, 34, 46, 47, 51, 59, 62], "pair": [10, 18, 19, 21, 22, 25, 27, 29, 31, 32, 33, 41, 52, 54, 65], "sale": [10, 14, 29, 58], "suppos": [10, 11, 13, 26, 39, 40, 54, 62], "independ": [10, 13, 34, 38, 39, 46, 48, 54, 61], "6x_1": 10, "6x_2": 10, "reason": [10, 18, 27, 29, 30, 34, 35, 41, 44, 45, 48, 49, 52, 54, 56, 59, 62, 63, 67, 68], "bim_with_revenues_minus_cost": 10, "minu": [10, 18, 33, 56, 65], "variable_cost": 10, "fixed_cost": 10, "bim_linear": 10, "3f": [10, 14, 17, 29, 37, 41, 43, 45, 46, 47, 49, 51, 56, 57, 67], "15925": 10, "000": [10, 14, 17, 32, 38, 44, 45, 54, 57, 61, 63], "1775": 10, "ratio": 10, "dfrac": 10, "deal": [10, 54], "auxiliari": [10, 11, 12, 14, 18, 22, 33, 41, 44, 46, 49, 54, 62], "12y_1": 10, "9y_2": 10, "cdot": [10, 18, 25, 26, 29, 30, 46, 49, 59, 61, 64, 65], "4y_1": 10, "2y_2": 10, "100y": 10, "despit": 10, "recov": [10, 30, 45, 64], "bim_with_revenues_over_cost": 10, "bim_fract": 10, "250": [10, 24, 34, 44, 54, 59, 63, 68], "051": [10, 29], "16500": 10, "1641": 10, "667": [10, 57], "972": 10, "strictli": [10, 52], "seemingli": 11, "rewritten": [11, 57], "maxima": [11, 38], "finit": [11, 13, 29, 39, 61, 62], "k": [11, 13, 18, 21, 22, 23, 24, 25, 27, 29, 30, 31, 33, 36, 39, 41, 43, 44, 45, 46, 48, 49, 59, 62], "c_k": 11, "minimax": 11, "max_": [11, 15, 38, 45, 46, 48, 58, 61, 68], "top_": 11, "latter": [11, 24, 65], "z": [11, 15, 16, 18, 22, 26, 29, 36, 38, 41, 43, 44, 46, 49, 54, 56, 58, 59, 61, 62], "qquad": [11, 15, 20, 23, 25, 38, 39, 41, 44, 52, 61, 64], "guarante": [11, 14, 15, 32, 37, 43, 47, 54, 56, 57, 61, 62, 64], "vice": [11, 62], "versa": [11, 62], "henc": [11, 34, 61, 65], "uncertainti": [11, 15, 42, 43, 51, 60, 62, 64, 65], "regard": [11, 15, 25, 38, 41, 44, 49, 50, 54, 63], "instead": [11, 16, 20, 29, 30, 34, 35, 40, 54, 56, 61, 62, 64, 65, 67], "nomin": [11, 51, 54, 62, 63, 64, 65], "estim": [11, 13, 38, 50, 56, 59, 62], "trick": [11, 44], "bim_maxmin": 11, "maxmin": [11, 15], "c1": [11, 21], "c2": [11, 21], "583": [11, 15, 29], "1166": [11, 15, 54], "17500": [11, 15], "carefulli": [12, 38, 51], "manag": [12, 43, 44, 45, 46, 48, 50, 51, 55, 68], "led": [12, 51], "monthli": [12, 18, 51], "jan": [12, 18, 51], "feb": [12, 18, 51], "mar": [12, 18, 51], "jun": [12, 18, 51], "jul": [12, 18, 51], "aug": [12, 18, 51], "oct": [12, 18, 51], "nov": [12, 18, 51], "dec": [12, 18, 51, 62], "88": [12, 18, 20, 29, 34, 36, 51, 61, 65], "125": [12, 18, 34, 36, 41, 48, 51, 54, 61, 65], "260": [12, 18, 32, 51, 63], "217": [12, 18, 29, 51, 65], "238": [12, 17, 18, 29, 51, 63], "286": [12, 18, 51], "248": [12, 18, 51], "265": [12, 18, 51, 54, 65], "293": [12, 18, 29, 51], "259": [12, 18, 27, 51], "244": [12, 18, 51], "62": [12, 18, 29, 36, 51, 64], "81": [12, 18, 29, 32, 34, 38, 41, 51, 62], "95": [12, 18, 29, 32, 34, 41, 44, 46, 49, 51, 61, 64], "118": [12, 18, 24, 29, 31, 34, 45, 46, 48, 51, 65], "86": [12, 18, 24, 29, 34, 36, 41, 43, 51, 65], "89": [12, 18, 29, 51], "82": [12, 18, 29, 51, 59], "84": [12, 18, 29, 36, 41, 51, 59, 65], "200": [12, 14, 18, 20, 27, 31, 34, 38, 43, 44, 51, 54, 58, 61, 63, 65, 68], "acquir": [12, 18, 38, 51], "month": [12, 18, 51], "9000": [12, 51, 63], "05": [12, 18, 22, 24, 25, 26, 29, 36, 37, 38, 41, 43, 44, 45, 47, 48, 49, 51, 54, 57, 59, 61, 64, 65], "spend": [12, 51], "5000": [12, 23, 44, 45, 51, 59, 63], "kept": [12, 18, 51], "datafram": [12, 14, 15, 18, 20, 21, 22, 24, 25, 27, 29, 30, 31, 32, 34, 36, 38, 40, 41, 43, 44, 48, 49, 51, 52, 54, 56, 58, 61, 62, 63, 65, 67, 68], "stringio": [12, 18, 31, 51], "ipython": [12, 30, 32, 33, 34, 37, 47, 57, 64], "demand_data": [12, 18, 51], "price_data": [12, 51], "demand_chip": [12, 18, 51], "index_col": [12, 18, 29, 31, 34, 51, 56, 64, 65], "consumpt": [12, 40], "dict": [12, 18, 20, 31, 36, 38, 41, 44, 49, 51, 56, 59], "from_dict": [12, 18, 51, 56, 58, 67], "fillna": [12, 14, 18, 38, 51, 63], "astyp": [12, 18, 25, 30, 36, 51], "int": [12, 13, 18, 19, 27, 29, 30, 36, 39, 46, 48, 51, 52, 59, 67], "135": [12, 18, 29, 32, 44, 59, 64, 68], "187": [12, 18, 48], "341": [12, 18], "282": [12, 18], "333": [12, 15, 18, 45], "404": [12, 18], "334": [12, 18, 36, 48], "327": [12, 18, 51], "347": [12, 18, 29], "375": [12, 18, 51, 54, 63, 68], "343": [12, 18, 29, 51, 54], "446": [12, 18], "624": [12, 18], "1202": [12, 18], "998": [12, 18, 29], "1142": [12, 18, 45, 48], "1380": [12, 18, 36], "1164": [12, 18, 51], "1130": [12, 18], "1224": [12, 18], "1336": [12, 18], "1204": [12, 18], "1108": [12, 18], "pt": [12, 18, 51], "s_": [12, 14, 18, 32, 36], "uniqu": [12, 19, 21, 22, 25, 29, 34, 41, 49, 58, 65, 68], "eas": [12, 64], "pi_": [12, 18, 36], "h_": [12, 20], "happen": [12, 25, 38, 42, 54, 61, 64, 65, 67], "constant": [12, 19, 24, 26, 29, 30, 39, 43, 44, 56, 58, 59, 61, 65, 68], "focu": [12, 30, 41], "beta": [12, 18, 26, 43, 57], "further": [12, 13, 18, 21, 29, 36, 39, 44, 45, 49, 52, 59, 61], "storag": 12, "ell": [12, 54], "balanc": [12, 18, 29, 30, 31, 33, 38, 40, 41, 51, 54, 56, 63, 64, 65], "delta_": [12, 18], "impli": [12, 34, 40, 41, 44, 62], "period": [12, 14, 18, 21, 25, 29, 45, 51, 65], "texttt": 12, "januari": 12, "captur": [12, 34, 37, 61, 67], "level": [12, 14, 18, 21, 24, 34, 43, 47, 50, 51, 52, 56, 57, 59, 61, 64, 65], "decemb": 12, "textrm": [12, 18], "omega_p": [12, 18], "bimproductacquisitionandinventori": [12, 51], "acquisition_pric": [12, 51], "exist": [12, 18, 22, 29, 30, 31, 32, 34, 41, 45, 49, 51, 65, 67, 68], "stock_limit": [12, 18, 51], "month_budget": [12, 51], "prev": [12, 18, 51], "bloat": [12, 18, 51], "pi": [12, 18, 44, 51], "h": [12, 25, 27, 29, 36, 43, 44, 45, 48, 51, 52, 59, 67, 68], "delta": [12, 14, 18, 29, 51, 54, 56, 64], "acquisition_cost": [12, 18, 51], "inventory_cost": [12, 18, 51], "total_cost": [12, 18, 32, 51], "2000": [12, 18, 20, 21, 22, 24, 31, 36, 40, 44, 48, 51, 54, 56, 63], "show_table_of_pyomo_vari": 12, "from_record": [12, 18, 51], "report_pyomo_solut": 12, "nthe": 12, "equidistant_color": 12, "drawstyl": [12, 18, 51], "mid": [12, 18, 51], "set_xtick": [12, 25, 52], "tick": [12, 36, 51], "set_xticklabel": [12, 25], "965": 12, "1078": 12, "266": [12, 18, 51], "1310": [12, 18, 51], "3548": 12, "962": 12, "4312": 12, "912": [12, 18, 51], "787": [12, 18, 59], "527": [12, 18], "1275": [12, 54], "1037": 12, "751": 12, "503": 12, "785": 12, "744": [12, 51], "1615": [12, 18, 51], "1428": [12, 18, 51], "1087": [12, 18, 44, 51], "805": [12, 18, 51], "472": [12, 18, 51], "4354": [12, 18], "3730": [12, 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