diff --git a/_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png b/_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png deleted file mode 100644 index 025c11c8..00000000 Binary files a/_images/389430d536e5ac77bdea65dd50aa87e6a0f5d1fd141193ac623b0c4b552f39ab.png and /dev/null differ diff --git a/_images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png b/_images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png deleted file mode 100644 index d218e255..00000000 Binary files a/_images/3b2327d53bb3b60571b2b9ef5752107607ce6477043c6d88d4b24d99edb81d67.png and /dev/null differ diff --git a/_images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png b/_images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png new file mode 100644 index 00000000..9626a0de Binary files /dev/null and b/_images/47887ea045828ae0d35ed80594a5feeb881571ee6c18223cc66eddc73637a204.png differ diff --git a/_images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg b/_images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg new file mode 100644 index 00000000..349af443 --- /dev/null +++ b/_images/4cf03a10538e7518e058b3d9435174e177a3d404163772d73bf74d8d1f58eadb.svg @@ -0,0 +1,191 @@ + + + + + + + + + +Terminal A + +Terminal A +supply = 100000 +shipped = 100000.0 +sens  = -0.0045 + + + +Alice + +Alice +demand = 30000 +shipped = 30000.0 +sens  = 0.0875 + + + +Terminal A->Alice + + +rate = 8.3 +shipped = 30000.0 + + + +Badri + +Badri +demand = 40000 +shipped = 40000.0 +sens  = 0.0855 + + + +Terminal A->Badri + + +rate = 8.1 +shipped = 40000.0 + + + +Cara + +Cara +demand = 50000 +shipped = 50000.0 +sens  = 0.0875 + + + +Terminal A->Cara + + +rate = 8.3 +shipped = 12000.0 + + + +Helen + +Helen +demand = 18000 +shipped = 18000.0 +sens  = 0.0795 + + + +Terminal A->Helen + + +rate = 7.5 +shipped = 18000.0 + + + +Terminal B + +Terminal B +supply = 80000 +shipped = 80000.0 +sens  = -0.0075 + + + +Dan + +Dan +demand = 20000 +shipped = 20000.0 +sens  = 0.0875 + + + +Terminal B->Dan + + +rate = 8.0 +shipped = 20000.0 + + + +Grace + +Grace +demand = 80000 +shipped = 80000.0 +sens  = 0.0875 + + + +Terminal B->Grace + + +rate = 8.0 +shipped = 60000.0 + + + +Current Supplier + +Current Supplier +supply = 500000 +shipped = 133000.0 +sens  = 0.0 + + + +Current Supplier->Cara + + +rate = 8.75 +shipped = 38000.0 + + + +Emma + +Emma +demand = 30000 +shipped = 30000.0 +sens  = 0.0875 + + + +Current Supplier->Emma + + +rate = 8.75 +shipped = 30000.0 + + + +Fujita + +Fujita +demand = 45000 +shipped = 45000.0 +sens  = 0.0875 + + + +Current Supplier->Fujita + + +rate = 8.75 +shipped = 45000.0 + + + +Current Supplier->Grace + + +rate = 8.75 +shipped = 20000.0 + + + \ No newline at end of file diff --git a/_images/92dd3c75b6470b3594ef89e39a98e0e1854792398ec0ff2899b31056e56cc519.png 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--git a/_images/seating_flow_model.png b/_images/seating_flow_model.png deleted file mode 100644 index 5e738629..00000000 Binary files a/_images/seating_flow_model.png and /dev/null differ diff --git a/_images/seating_model_basic.png b/_images/seating_model_basic.png deleted file mode 100644 index de14615f..00000000 Binary files a/_images/seating_model_basic.png and /dev/null differ 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
  | Demand
  | Terminal A
100,000| Terminal B
80,000 | Current Supplier
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", + "
\n", + "\n", + "| Franchisee
  | Demand
  | Current Supplier
500,000 | Terminal A
100,000| Terminal B
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", + "
\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": [ - "
Transportation Rates (€ cents per Gallon)" + "
Transportation Rates ($ cents per Gallon)" ], "text/plain": [ "" @@ -464,7 +477,7 @@ "display(HTML(\"
Gasoline Demand (Gallons)\"))\n", "display(demand.to_frame())\n", "\n", - "display(HTML(\"
Transportation Rates (€ cents per Gallon)\"))\n", + "display(HTML(\"
Transportation Rates ($ cents per Gallon)\"))\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 @@ " \n", " \n", " Alice\n", - " 30000.0\n", - " 0.0\n", - " 0.0\n", + " 30000\n", + " 0\n", + " 0\n", " 2625.0\n", " 2490.0\n", " 135.0\n", - " 0.0830\n", - " 0.0875\n", + " 8.30\n", + " 8.75\n", " \n", " \n", " Badri\n", - " 40000.0\n", - " 0.0\n", - " 0.0\n", + " 40000\n", + " 0\n", + " 0\n", " 3500.0\n", " 3240.0\n", " 260.0\n", - " 0.0810\n", - " 0.0855\n", + " 8.10\n", + " 8.55\n", " \n", " \n", " Cara\n", - " 12000.0\n", - " 0.0\n", - " 38000.0\n", + " 12000\n", + " 0\n", + " 38000\n", " 4375.0\n", " 4321.0\n", " 54.0\n", - " 0.0864\n", - " 0.0875\n", + " 8.64\n", + " 8.75\n", " \n", " \n", " Dan\n", - " 0.0\n", - " 20000.0\n", - " 0.0\n", + " 0\n", + " 20000\n", + " 0\n", " 1750.0\n", " 1600.0\n", " 150.0\n", - " 0.0800\n", - " 0.0875\n", + " 8.00\n", + " 8.75\n", " \n", " \n", " Emma\n", - " 0.0\n", - " 0.0\n", - " 30000.0\n", + " 0\n", + " 0\n", + " 30000\n", " 2625.0\n", " 2625.0\n", " 0.0\n", - " 0.0875\n", - " 0.0875\n", + " 8.75\n", + " 8.75\n", " \n", " \n", " Fujita\n", - " 0.0\n", - " 0.0\n", - " 45000.0\n", + " 0\n", + " 0\n", + " 45000\n", " 3937.5\n", " 3937.5\n", " 0.0\n", - " 0.0875\n", - " 0.0875\n", + " 8.75\n", + " 8.75\n", " \n", " \n", " Grace\n", - " 0.0\n", - " 60000.0\n", - " 20000.0\n", + " 0\n", + " 60000\n", + " 20000\n", " 7000.0\n", " 6550.0\n", " 450.0\n", - " 0.0819\n", - " 0.0875\n", + " 8.19\n", + " 8.75\n", " \n", " \n", " Helen\n", - " 18000.0\n", - " 0.0\n", - " 0.0\n", + " 18000\n", + " 0\n", + " 0\n", " 1575.0\n", " 1350.0\n", " 225.0\n", - " 0.0750\n", - " 0.0795\n", + " 7.50\n", + " 7.95\n", " \n", " \n", "\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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AlCOlDijNmjXTxo0btXbtWg0YMED9+vXTtm3bPFGby+jRo5WTk+PaMjMzPXo8AABgL7/SviAgIEBNmjSRJLVr105paWmaMmWKHnroIRUUFOjIkSNuoyjZ2dmKiIiQJEVEROinn35y+3xn7vI50+d8AgMDFRgYWNpSAQBAOXXF66AUFRUpPz9f7dq1k7+/v5KTk11tO3bsUEZGhmJiYiRJMTEx2rx5sw4ePOjqs3z5cjmdTkVFRV1pKQAAwEuUagRl9OjRio+PV2RkpI4ePap58+bp+++/1zfffKPg4GA98cQTSkxMVGhoqJxOpwYPHqyYmBh17NhRktS1a1dFRUWpb9++mjRpkrKysjRmzBgNHDiQERIAAOBSqoBy8OBBPfroozpw4ICCg4PVsmVLffPNN7rjjjskSZMnT5aPj4969uyp/Px8xcXFafr06a7X+/r6asmSJRowYIBiYmJUtWpV9evXT+PGjbu6ZwUAAMq1K14HxQ6sgwIAOIN1UMqPMlkHBQAAwFMIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHH87C4AZavhqK9sO/a+iQm2HRsAUL4wggIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMbxs7sAAABQeg1HfWXbsfdNTPD4MRhBAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABinVAHllVde0Q033KCgoCCFhYXp3nvv1Y4dO9z6nDhxQgMHDlSNGjVUrVo19ezZU9nZ2W59MjIylJCQoCpVqigsLEzDhw/XqVOnrvxsAACAVyhVQFm1apUGDhyoNWvWaPny5Tp58qS6du2qvLw8V59hw4Zp8eLF+vzzz7Vq1Srt379fPXr0cLUXFhYqISFBBQUFSklJ0QcffKA5c+YoKSnp6p0VAAAo1/xK03nZsmVuH8+ZM0dhYWFKT0/XrbfeqpycHM2cOVPz5s1T586dJUmzZ89W8+bNtWbNGnXs2FHffvuttm3bpu+++07h4eFq3bq1xo8fr5EjR+qll15SQEBAsePm5+crPz/f9XFubu7lnCsAACgnrmgOSk5OjiQpNDRUkpSenq6TJ08qNjbW1ee6665TZGSkUlNTJUmpqamKjo5WeHi4q09cXJxyc3O1devW8x7nlVdeUXBwsGurX7/+lZQNAAAMd9kBpaioSM8995xuvvlmXX/99ZKkrKwsBQQEKCQkxK1veHi4srKyXH3ODidn2s+0nc/o0aOVk5Pj2jIzMy+3bAAAUA6U6hLP2QYOHKgtW7bohx9+uJr1nFdgYKACAwM9fhwAAGCGyxpBGTRokJYsWaKVK1eqXr16rv0REREqKCjQkSNH3PpnZ2crIiLC1efcu3rOfHymDwAAqNhKFVAsy9KgQYP0xRdfaMWKFWrUqJFbe7t27eTv76/k5GTXvh07digjI0MxMTGSpJiYGG3evFkHDx509Vm+fLmcTqeioqKu5FwAAICXKNUlnoEDB2revHn68ssvFRQU5JozEhwcrMqVKys4OFhPPPGEEhMTFRoaKqfTqcGDBysmJkYdO3aUJHXt2lVRUVHq27evJk2apKysLI0ZM0YDBw7kMg4AAJBUyoDy9ttvS5Juv/12t/2zZ8/WY489JkmaPHmyfHx81LNnT+Xn5ysuLk7Tp0939fX19dWSJUs0YMAAxcTEqGrVqurXr5/GjRt3ZWcCAAC8RqkCimVZF+1TqVIlTZs2TdOmTSuxT4MGDfT111+X5tAAAKAC4Vk8AADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBx/OwuAIDnNBz1lW3H3jcxwbZjAyj/GEEBAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAONU2KXuWQIcAABzMYICAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTqkDyurVq9W9e3fVqVNHDodDixYtcmu3LEtJSUmqXbu2KleurNjYWP373/9263P48GH17t1bTqdTISEheuKJJ3Ts2LErOhEAAOA9Sh1Q8vLy1KpVK02bNu287ZMmTdLUqVM1Y8YMrV27VlWrVlVcXJxOnDjh6tO7d29t3bpVy5cv15IlS7R69Wo9/fTTl38WAADAq/iV9gXx8fGKj48/b5tlWXrjjTc0ZswY3XPPPZKkDz/8UOHh4Vq0aJF69eql7du3a9myZUpLS1P79u0lSW+++aa6deumV199VXXq1LmC0wEAAN7gqs5B2bt3r7KyshQbG+vaFxwcrBtvvFGpqamSpNTUVIWEhLjCiSTFxsbKx8dHa9euPe/nzc/PV25urtsGAAC811UNKFlZWZKk8PBwt/3h4eGutqysLIWFhbm1+/n5KTQ01NXnXK+88oqCg4NdW/369a9m2QAAwDDl4i6e0aNHKycnx7VlZmbaXRIAAPCgqxpQIiIiJEnZ2dlu+7Ozs11tEREROnjwoFv7qVOndPjwYVefcwUGBsrpdLptAADAe13VgNKoUSNFREQoOTnZtS83N1dr165VTEyMJCkmJkZHjhxRenq6q8+KFStUVFSkG2+88WqWAwAAyqlS38Vz7Ngx7dq1y/Xx3r17tXHjRoWGhioyMlLPPfec/vM//1NNmzZVo0aN9OKLL6pOnTq69957JUnNmzfXnXfeqaeeekozZszQyZMnNWjQIPXq1Ys7eAAAgKTLCCjr1q1Tp06dXB8nJiZKkvr166c5c+ZoxIgRysvL09NPP60jR47ob3/7m5YtW6ZKlSq5XjN37lwNGjRIXbp0kY+Pj3r27KmpU6dehdMBAADeoNQB5fbbb5dlWSW2OxwOjRs3TuPGjSuxT2hoqObNm1faQwMAgAqiXNzFAwAAKhYCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxvGzuwCgLDQc9ZVtx943McG2YwNAecUICgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADj2BpQpk2bpoYNG6pSpUq68cYb9dNPP9lZDgAAMIRtAeXTTz9VYmKixo4dq/Xr16tVq1aKi4vTwYMH7SoJAAAYwraA8vrrr+upp55S//79FRUVpRkzZqhKlSqaNWuWXSUBAABD+Nlx0IKCAqWnp2v06NGufT4+PoqNjVVqamqx/vn5+crPz3d9nJOTI0nKzc297BqK8o9f9muv1JXUfaU477LHeQOeVVHf5+XxvM+8zrKsi3e2bPDbb79ZkqyUlBS3/cOHD7c6dOhQrP/YsWMtSWxsbGxsbGxesGVmZl40K9gyglJao0ePVmJiouvjoqIiHT58WDVq1JDD4SjTWnJzc1W/fn1lZmbK6XSW6bHtxHlz3hUB5815VwR2nrdlWTp69Kjq1Klz0b62BJSaNWvK19dX2dnZbvuzs7MVERFRrH9gYKACAwPd9oWEhHiyxItyOp0V6g19BuddsXDeFQvnXbHYdd7BwcGX1M+WSbIBAQFq166dkpOTXfuKioqUnJysmJgYO0oCAAAGse0ST2Jiovr166f27durQ4cOeuONN5SXl6f+/fvbVRIAADCEbQHloYce0qFDh5SUlKSsrCy1bt1ay5YtU3h4uF0lXZLAwECNHTu22CUnb8d5c94VAefNeVcE5eW8HZZ1Kff6AAAAlB2exQMAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYJxy8Sweu+3evVuzZ8/W7t27NWXKFIWFhWnp0qWKjIxUixYt7C4PHpCXl6dVq1YpIyNDBQUFbm1DhgyxqSoAV8ORI0e0YMEC7d69W8OHD1doaKjWr1+v8PBw1a1b1+7y8H9YB+UiVq1apfj4eN18881avXq1tm/frsaNG2vixIlat26dFixYYHeJV9XUqVP19NNPq1KlSpo6deoF+3rrL+oNGzaoW7duOn78uPLy8hQaGqrff/9dVapUUVhYmPbs2WN3iR6RnZ2t559/XsnJyTp48GCxx6EXFhbaVFnZOXHiRLFA6s3PaFm3bp0+++yz8wbxhQsX2lSVZ/3888+KjY1VcHCw9u3bpx07dqhx48YaM2aMMjIy9OGHH9pdokfk5eVp4sSJru/voqIit3YTf64RUC4iJiZGDzzwgBITExUUFKRNmzapcePG+umnn9SjRw/9z//8j90lXlWNGjXSunXrVKNGDTVq1KjEfg6Hw8g39NVw++2369prr9WMGTMUHBysTZs2yd/fX3369NHQoUPVo0cPu0v0iPj4eGVkZGjQoEGqXbt2sSeF33PPPTZV5lnHjx/XiBEj9Nlnn+mPP/4o1u6twWz+/Pl69NFHFRcXp2+//VZdu3bVzp07lZ2drfvuu0+zZ8+2u0SPiI2NVdu2bTVp0iS3n+kpKSl65JFHtG/fPrtL9IiHH35Yq1atUt++fc/7/T106FCbKrsACxdUtWpVa8+ePZZlWVa1atWs3bt3W5ZlWXv37rUCAwPtLA0eEhwcbP3yyy+uf2/bts2yLMtas2aN1axZMztL86hq1apZGzZssLuMMvfss89azZs3txYsWGBVrlzZmjVrljV+/HirXr161scff2x3eR4THR1tvfXWW5Zl/f/PtqKiIuupp56ykpKSbK7Oc5xOp7Vr1y7Lstx/pu/bt8+rf6YHBwdbP/zwg91llAqTZC8iJCREBw4cKLZ/w4YNXn2t8uTJk7rmmmu0fft2u0spc/7+/vLxOf2tERYWpoyMDEmnHxGemZlpZ2keVb9+/WKXdSqCxYsXa/r06erZs6f8/Px0yy23aMyYMZowYYLmzp1rd3kes3v3biUkJEg6/YT5vLw8ORwODRs2TO+++67N1XlOYGCgcnNzi+3fuXOnatWqZUNFZaN69eoKDQ21u4xSIaBcRK9evTRy5EhlZWXJ4XCoqKhIP/74o55//nk9+uijdpfnMf7+/jpx4oTdZdiiTZs2SktLkyTddtttSkpK0ty5c/Xcc8/p+uuvt7k6z3njjTc0atQorx3iLsnhw4fVuHFjSafnmxw+fFiS9Le//U2rV6+2szSPql69uo4ePSpJqlu3rrZs2SLp9ATS48eP21maR919990aN26cTp48Ken05eqMjAyNHDlSPXv2tLk6zxk/frySkpLK19fW7iEc0+Xn51tPPvmk5efnZzkcDsvf39/y8fGx+vTpY506dcru8jzqH//4h9WvXz/r5MmTdpdSptLS0qwVK1ZYlmVZ2dnZVlxcnBUUFGS1bdvW2rhxo83VeU5ISIgVEBBg+fj4WNWqVbOqV6/utnmr6Oho6/vvv7csy7K6dOli/f3vf7csy7KmTJli1a1b187SPOrhhx+2XnvtNcuyLGvcuHFWrVq1rCeffNJq0KCBdd9999lcneccOXLEio2NtUJCQixfX1+rfv36lr+/v3Xrrbdax44ds7s8j2ndurUVFBRkVatWzbr++uutNm3auG0mYpLsJcrMzNTmzZt17NgxtWnTRk2bNrW7JI+77777lJycrGrVqik6OlpVq1Z1a/fGWf6WZSkzM1NhYWGqVKmS3eWUqQ8++OCC7f369SujSsrW5MmT5evrqyFDhui7775T9+7dZVmWTp48qddff93MyYNXweHDh3XixAnVqVNHRUVFmjRpklJSUtS0aVONGTNG1atXt7tEj/rxxx+1adMmHTt2TG3btlVsbKzdJXnUyy+/fMH2sWPHllEll46AghL179//gu3eOMu/qKhIlSpV0tatWytECEVxv/76q9LT09WkSRO1bNnS7nKACouAchE9e/ZUhw4dNHLkSLf9kyZNUlpamj7//HObKoOntGjRQjNnzlTHjh3tLsU2FW09kIrI19dXBw4cUFhYmNv+P/74Q2FhYV57e/WQIUPUpEmTYus4vfXWW9q1a5feeOMNeworA+VtgToCykXUqlVLK1asUHR0tNv+zZs3KzY2VtnZ2TZVBk9ZvHixJk2apLfffturJ8WeKy8vTyNHjqxw64FIUlpamlauXHneBaxef/11m6ryLB8fH2VlZRULKPv379c111yjv/76y6bKPKtu3br617/+pXbt2rntX79+ve6++26vW9vqjPK4QB1L3V/EsWPHFBAQUGy/v7//eW9VK+/atGlTbAGfkqxfv97D1djj0Ucf1fHjx9WqVSsFBASocuXKbu1n7vLwNiNGjNDKlSv19ttvq2/fvpo2bZp+++03vfPOO5o4caLd5XnMhAkTNGbMGDVr1kzh4eFu7/9L/V4oT86sEO1wOPT++++rWrVqrrbCwkKtXr1a1113nV3ledwff/yh4ODgYvudTqd+//13GyoqG4mJiXrsscdcC9Sd0a1bNz3yyCM2VlYyAspFREdH69NPP1VSUpLb/vnz5ysqKsqmqjzn3nvvdf37xIkTmj59uqKiohQTEyNJWrNmjbZu3apnn33Wpgo9z5uHeC9k8eLF+vDDD3X77berf//+uuWWW9SkSRM1aNBAc+fOVe/eve0u0SOmTJmiWbNm6bHHHrO7lDIxefJkSacnhM+YMUO+vr6utoCAADVs2FAzZsywqzyPa9KkiZYtW6ZBgwa57V+6dKnrdnNvlJaWpnfeeafY/rp16yorK8uGii6OgHIRL774onr06KHdu3erc+fOkqTk5GR98sknXjn/5OyZ3E8++aSGDBmi8ePHF+vjzQuWeevdKhdzofVABgwYYGdpHuXj46Obb77Z7jLKzN69eyVJnTp10sKFC73+bp1zJSYmatCgQTp06JDbz/TXXnvNq/84KZcL1Nl0e3O5smTJEuumm26yqlSpYtWoUcPq1KmTa90Eb+Z0Oq2dO3cW279z507L6XTaUFHZ++uvv6ycnBy3zVtV1PVA/vnPf1pDhw61uwyUoenTp1t169a1HA6H5XA4rEaNGlkffPCB3WV51BNPPGHde++9VkFBgVWtWjVrz5491q+//mq1adPG2Pc/k2RRooiICE2cOLHY0PecOXM0cuRIr50gXFEni1bU9UCKioqUkJCgnTt3KioqSv7+/m7t3rTeT2JiosaPH6+qVasqMTHxgn29dXLw2Q4dOqTKlSu7zcPxVjk5Obr//vu1bt06HT16VHXq1FFWVpZiYmL09ddfF1vnygRc4kGJnnvuOQ0YMEDr169Xhw4dJElr167VrFmz9OKLL9pcnedU1Mmiw4YNc/07NjZWv/zyS4VYD2TIkCFauXKlOnXqpBo1anjlxNgzNmzY4FrifcOGDSX28+b/g7MZe2nDA4KDg7V8+XL98MMP+vnnn8vFAnWMoJxHaGiodu7cqZo1a6p69eoX/Gb11js6zvjss880ZcoU10MDmzdvrqFDh+rBBx+0uTLPiYyMdE0WdTqdWr9+vZo0aaKPPvpIn3zyib7++mu7S7zqioqKNGfOHC1cuFD79u2Tw+FQo0aNdP/996tv375e/QsrKChI8+fPdz04D95vwYIF+uyzz5SRkVFsvR9vvTuxPGIE5TwmT57sug3LmydNXYoHH3zQq8PI+VS0yaKWZenuu+/W119/rVatWik6OlqWZWn79u167LHHtHDhQi1atMjuMj0mNDRU11xzjd1loIxMnTpVL7zwgh577DF9+eWX6t+/v3bv3q20tDQNHDjQ7vKuqjO3lF+KcxeuMwEjKMA5WrZsqTfffFO33XabYmNj1bp1a7366quaOnWqJk2a5HULOc2ePVtDhw7Vl19+qU6dOrm1rVixQvfee6/eeustr3169+zZs7Vs2TLNnj1bVapUsbscj+rRo4fmzJkjp9OpHj16XLBvtWrV1KJFCz3zzDPnXTekvLruuus0duxYPfzwwwoKCtKmTZvUuHFjJSUl6fDhw3rrrbfsLvGqadSo0SX1czgc2rNnj4erKT0CynmUZgE2b17+u7CwUJMnTy5xKNRbL2+VNFm0oKBAkydP9rrJol27dlXnzp01atSo87ZPmDBBq1at0jfffFPGlZWNNm3aaPfu3bIsSw0bNiw2Sdabhvz79++vqVOnKigo6KLP2srPz1dqaqqio6P1r3/9q4wq9LwqVapo+/btatCggcLCwrR8+XK1atVK//73v9WxY8fzToyHPbjEcx4hISEXveZuWZYcDofX3tEhnX765fvvv6+///3vGjNmjF544QXt27dPixYtKrZwnTcpabJo06ZNiz3ywBv8/PPPmjRpUont8fHxpRoqLm/OXpzQ2539gM9Ledjntm3bdMMNN3iypDIXERGhw4cPq0GDBoqMjNSaNWvUqlUr7d27VxXh7/WCggLt3btX11xzjfz8zI4AjKCcx6pVqy6p3+bNm4utRuhNrrnmGk2dOlUJCQkKCgrSxo0bXfvWrFmjefPm2V3iVbVixQoNGjRIa9asKTYylpOTo5tuukkzZszQLbfcYlOFnhEQEKBff/1VtWvXPm/7/v371ahRI+Xn55dxZbBbYWGhtmzZolatWtldylXz5JNPqn79+ho7dqymTZum4cOH6+abb9a6devUo0cPzZw50+4SPeL48eMaPHiwPvjgA0mnF2hr3LixBg8erLp165Y4gmonAkopHT16VJ988onef/99paene/UIStWqVbV9+3ZFRkaqdu3a+uqrr9S2bVvt2bNHbdq0UU5Ojt0lXlV33323OnXq5DaCcrapU6dq5cqV+uKLL8q4Ms/y9fVVVlZWibdcZmdnq06dOl79Xj/j2LFjxR4W6K2XcTt16nTBkeIVK1aUYTVlp6ioSEVFRa7Rg/nz5yslJUVNmzbVf/zHf5z32WveYOjQofrxxx/1xhtv6M4779TPP/+sxo0b68svv9RLL710wdvO7WL2+I5BVq9erZkzZ+q///u/VadOHfXo0UPTpk2zuyyPqlevng4cOKDIyEhdc801+vbbb9W2bVulpaUpMDDQ7vKuuk2bNumf//xnie1du3bVq6++WoYVlQ3LsvTYY4+V+DX19pGTvXv3atCgQfr+++914sQJ135vv4zbunVrt49PnjypjRs3asuWLV77uIdTp05pwoQJevzxx1WvXj1JUq9evdSrVy+bK/O8RYsW6dNPP1XHjh3dgmmLFi20e/duGysrGQHlArKysjRnzhzNnDlTubm5evDBB5Wfn69FixZ55YMCz3XfffcpOTlZN954owYPHqw+ffpo5syZysjIKHGUoTzLzs4uNkHybH5+fjp06FAZVlQ2LuWXkbfewSNJffr0kWVZmjVrVrGnGXuzMw8NPNdLL72kY8eOlXE1ZcPPz0+TJk3y6vdzSQ4dOqSwsLBi+/Py8ox9zxNQStC9e3etXr1aCQkJriExX19fr37K57nOXjX1oYceUoMGDVxDod27d7exMs+oW7eutmzZoiZNmpy3/eeffy5xnkZ5dimTJb3Zpk2blJ6ermbNmtldihH69OmjDh06eOVooSR16dJFq1atUsOGDe0upUy1b99eX331lQYPHizp/1cLfv/9911PqzcNAaUES5cu1ZAhQzRgwAA1bdrU7nJs8ccff6hGjRqSpMzMTH399df666+/1L59e5sr84xu3brpxRdf1J133qlKlSq5tf31118aO3as7rrrLpuqg6fccMMNyszMJKD8n9TU1GLvf28SHx+vUaNGafPmzWrXrl2xZ9DcfffdNlXmWRMmTFB8fLy2bdumU6dOacqUKdq2bZtSUlIu+caQssYk2RKsWbNGM2fO1KeffqrmzZurb9++6tWrl2rXrq1NmzZ59SWezZs3q3v37srMzFTTpk01f/583XnnncrLy5OPj4/y8vK0YMECr7s9Mzs7W23btpWvr68GDRrk+oX1yy+/aNq0aSosLNT69esVHh5uc6W4mnbv3q1nnnlGffr00fXXX1/sMp+3Pofo3IXaLMvSgQMHtG7dOr344osaO3asTZV5lo+PT4lt3jznSDr9Xp84caI2bdrkehbPyJEjjV0+gYByEXl5efr00081a9Ys/fTTTyosLNTrr7+uxx9/3LUcvreJj4+Xn5+fRo0apY8++khLlixRXFyc3nvvPUnS4MGDlZ6erjVr1thc6dX366+/asCAAfrmm29cayI4HA7FxcVp2rRpl7wyI8qPNWvW6JFHHtG+fftc+xwOh9dOkt2zZ48aNmyoJ554wm2/j4+PatWqpc6dO6tr1642VYer7VIXHjXxbjUCSins2LFDM2fO1EcffaQjR47ojjvu8KoVFs+oWbOmVqxYoZYtW+rYsWNyOp1KS0tTu3btJJ0eUejYsaOOHDlib6Ee9Oeff2rXrl2yLEtNmzZV9erV7S4JHhIVFaXmzZtrxIgR550k26BBA5sq8wxfX18dOHDANWHyoYce0tSpU71+ZPCvv/5ScnKy6zLt6NGj3e5Q8/Pz07hx47zu8paPj88FJ8GaHMQJKJehsLBQixcv1qxZs7wyoPj4+CgrK8v1A+zs51VIFWtdDHi/qlWratOmTSVOjvY2535/O51Obdy40fX97a1mzJihr776SosXL5Z0+udaixYtVLlyZUmn//AaPny4EhMT7Szzqjt7follWerWrZvef/991a1b163fbbfdVtalXRSTZC+Dr6+v7r33Xq+bg3G2cxO3qbehAVeqc+fOFSqgnKui/I06d+5cjRgxwm3fvHnzXMHs448/1rRp07wuoJwbPHx9fdWxY8dyEUgJKDivsxfuOnHihJ555hnXbHdvX7gLFUv37t01bNgwbd68WdHR0cUmyXrbXR0Oh6NC/gGya9cut8mglSpVcpsw26FDBw0cONCO0lACLvGgmIs95fSMir5+BrxDRburw8fHR/Hx8a4/QBYvXqzOnTsXu9124cKFdpTnMZUrV9bGjRtLvJ38l19+UevWrd1WE/ZG516yNxkjKCiG4IGK5Nxn73i7c1cO7tOnj02VlK169eppy5YtJQaUn3/+2bX8vbcrLyNmjKAAqJC6deumTz75RMHBwZJOr5z8zDPPKCQkRNLphQpvueUWbdu2zcYqcbUMHTpU3333ndLT08+7EGP79u0VGxurKVOm2FShZ5y73k15GjEjoACokM693fbcu1m4W827ZGdnq3Xr1goICNCgQYN07bXXSjq9fMRbb72lU6dOacOGDV53u3V5vmTPJR4AFdK5f5vxt5p3Cw8PV0pKigYMGKBRo0a5LcR4xx13aPr06V4XTiQzg8elIqAAACqERo0aadmyZTp8+LB27dolSWrSpIlCQ0NtrgznQ0ABUCFV1NttIYWGhqpDhw52l4GLIKAAqJAsy2K9H8BgTJIFUCGV58mDQEVAQAEAAMYpeQlFAAAAmxBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM87/2TXzG4QToAgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -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 @@ " 2625.0\n", " 2625.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Badri\n", @@ -824,19 +840,19 @@ " 3500.0\n", " 3500.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Cara\n", + " 49754.6\n", + " 245.4\n", " 0.0\n", - " 0.0\n", - " 50000.0\n", " 4375.0\n", " 4375.0\n", " 0.0\n", - " 0.0875\n", - " 0.000002\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Dan\n", @@ -846,8 +862,8 @@ " 1750.0\n", " 1750.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Emma\n", @@ -857,8 +873,8 @@ " 2625.0\n", " 2625.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Fujita\n", @@ -868,30 +884,30 @@ " 3937.5\n", " 3937.5\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Grace\n", - " 652.2\n", - " 79347.8\n", " 0.0\n", + " 0.0\n", + " 80000.0\n", " 7000.0\n", " 7000.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", " Helen\n", - " -0.0\n", + " 0.0\n", " 0.0\n", " 18000.0\n", " 1575.0\n", " 1575.0\n", " 0.0\n", - " 0.0875\n", - " 0.000000\n", + " 8.75\n", + " 0.0\n", " \n", " \n", "\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": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -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 @@ " 2625.0\n", " 2502.9\n", " 122.1\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Badri\n", @@ -1105,8 +1122,8 @@ " 3500.0\n", " 3337.2\n", " 162.8\n", - " 0.0834\n", - " 0.0855\n", + " 8.34\n", + " 8.55\n", " \n", " \n", " Cara\n", @@ -1116,8 +1133,8 @@ " 4375.0\n", " 4171.5\n", " 203.5\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Dan\n", @@ -1127,8 +1144,8 @@ " 1750.0\n", " 1668.6\n", " 81.4\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Emma\n", @@ -1138,8 +1155,8 @@ " 2625.0\n", " 2502.9\n", " 122.1\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Fujita\n", @@ -1149,8 +1166,8 @@ " 3937.5\n", " 3754.3\n", " 183.2\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Grace\n", @@ -1160,8 +1177,8 @@ " 7000.0\n", " 6674.4\n", " 325.6\n", - " 0.0834\n", - " 0.0875\n", + " 8.34\n", + " 8.75\n", " \n", " \n", " Helen\n", @@ -1171,8 +1188,8 @@ " 1575.0\n", " 1501.7\n", " 73.3\n", - " 0.0834\n", - " 0.0795\n", + " 8.34\n", + " 7.95\n", " \n", " \n", "\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": { - "image/png": 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", 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", 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" ] @@ -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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", 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" ] @@ -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": [ "
" ] @@ -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": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Terminal A\n", + "\n", + "Terminal A\n", + "supply = 100000\n", + "shipped = 100000.0\n", + "sens  = -0.0045\n", + "\n", + "\n", + "\n", + "Alice\n", + "\n", + "Alice\n", + "demand = 30000\n", + "shipped = 30000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Terminal A->Alice\n", + "\n", + "\n", + "rate = 8.3\n", + "shipped = 30000.0\n", + "\n", + "\n", + "\n", + "Badri\n", + "\n", + "Badri\n", + "demand = 40000\n", + "shipped = 40000.0\n", + "sens  = 0.0855\n", + "\n", + "\n", + "\n", + "Terminal A->Badri\n", + "\n", + "\n", + "rate = 8.1\n", + "shipped = 40000.0\n", + "\n", + "\n", + "\n", + "Cara\n", + "\n", + "Cara\n", + "demand = 50000\n", + "shipped = 50000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Terminal A->Cara\n", + "\n", + "\n", + "rate = 8.3\n", + "shipped = 12000.0\n", + "\n", + "\n", + "\n", + "Helen\n", + "\n", + "Helen\n", + "demand = 18000\n", + "shipped = 18000.0\n", + "sens  = 0.0795\n", + "\n", + "\n", + "\n", + "Terminal A->Helen\n", + "\n", + "\n", + "rate = 7.5\n", + "shipped = 18000.0\n", + "\n", + "\n", + "\n", + "Terminal B\n", + "\n", + "Terminal B\n", + "supply = 80000\n", + "shipped = 80000.0\n", + "sens  = -0.0075\n", + "\n", + "\n", + "\n", + "Dan\n", + "\n", + "Dan\n", + "demand = 20000\n", + "shipped = 20000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Terminal B->Dan\n", + "\n", + "\n", + "rate = 8.0\n", + "shipped = 20000.0\n", + "\n", + "\n", + "\n", + "Grace\n", + "\n", + "Grace\n", + "demand = 80000\n", + "shipped = 80000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Terminal B->Grace\n", + "\n", + "\n", + "rate = 8.0\n", + "shipped = 60000.0\n", + "\n", + "\n", + "\n", + "Current Supplier\n", + "\n", + "Current Supplier\n", + "supply = 500000\n", + "shipped = 133000.0\n", + "sens  = 0.0\n", + "\n", + "\n", + "\n", + "Current Supplier->Cara\n", + "\n", + "\n", + "rate = 8.75\n", + "shipped = 38000.0\n", + "\n", + "\n", + "\n", + "Emma\n", + "\n", + "Emma\n", + "demand = 30000\n", + "shipped = 30000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Current Supplier->Emma\n", + "\n", + "\n", + "rate = 8.75\n", + "shipped = 30000.0\n", + "\n", + "\n", + "\n", + "Fujita\n", + "\n", + "Fujita\n", + "demand = 45000\n", + "shipped = 45000.0\n", + "sens  = 0.0875\n", + "\n", + "\n", + "\n", + "Current Supplier->Fujita\n", + "\n", + "\n", + "rate = 8.75\n", + "shipped = 45000.0\n", + "\n", + "\n", + "\n", + "Current Supplier->Grace\n", + "\n", + "\n", + "rate = 8.75\n", + "shipped = 20000.0\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "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 @@

S

solver -

+

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 \(f\) to tables \(t\) so that everyone is assigned to some table, the tables’ capacities are respected, and no table gets more than \(k_{\max} = 3\) members of the same family.

\[\begin{split} @@ -1303,7 +1303,7 @@

Reformulation as max flow problem\(\sum_{f \in F} m_f\), it means that there exists a family-to-table assignment that meets our requirements.

-

+

Thus, if we maximize the total flow going out of door and reaching seat 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 maximum flow problem, 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 networkx.

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 @@

Contents

Gasoline distribution#

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.

-

Didactically, notebook presents techniques for Pyomo modeling and reporting including:

-
    -

  • pyo.Expression decorator

    • -

  • Accessing the duals (i.e., shadow prices)

    • -

  • Methods for reporting the solution and duals.

    -
      -

    • Pyomo .display() method for Pyomo objects

      • -

    • Manually formatted reports

      • -

    • Pandas

      • -

    • Graphviz for display of results as a directed graph.

      • -
    -
    • -

Preamble: Install Pyomo and a solver#

This cell selects and verifies a global SOLVER for the notebook.

@@ -551,87 +538,98 @@

Preamble: Install Pyomo and a solver 
import sys
-
+ 
 if 'google.colab' in sys.modules:
     !pip install pyomo >/dev/null 2>/dev/null
     !pip install highspy >/dev/null 2>/dev/null
-
-    from pyomo.environ import SolverFactory
-    SOLVER = SolverFactory('appsi_highs')
-    
-else:
-    from pyomo.environ import SolverFactory
-    SOLVER = SolverFactory('cbc')
-
-assert SOLVER.available(), f"Solver {SOLVER} is not available."
+ 
+solver = 'appsi_highs'
+ 
+import pyomo.environ as pyo
+SOLVER = pyo.SolverFactory(solver)
+ 
+assert SOLVER.available(), f"Solver {solver} is not available."

+

Didactically, this notebook presents techniques for Pyomo modeling and reporting including:

+
    +

  • pyo.Expression decorator

    • +

  • Accessing the duals (i.e., shadow prices)

    • +

  • Methods for reporting the solution and duals.

    +
      +

    • Pyomo .display() method for Pyomo objects

      • +

    • Manually formatted reports

      • +

    • Pandas

      • +

    • Graphviz for display of results as a directed graph.

      • +
    +
    • +

Problem: Distributing gasoline to franchise operators#

-

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.

+

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.

+

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.

The following chart shows the pricing of gasoline delivery in cents/gallon.

+
+ - + - + - + - + - + - + - + - + - @@ -641,18 +639,22 @@

Problem: Distributing gasoline to franchise operators

Franchisee
 

Demand
 

Current Supplier
500,000

Terminal A
100,000

Terminal B
80,000

Current Supplier
500,000

Alice

30,000

8.75

8.3

10.2

8.75

Badri

40,000

8.75

8.1

12.0

8.75

Cara

50,000

8.75

8.3

-

8.75

Dan

80,000

8.75

9.3

8.0

8.75

Emma

30,000

8.75

10.1

10.0

8.75

Fujita

45,000

8.75

9.8

10.0

8.75

Grace

80,000

8.75

-

8.0

8.75

Helen

18,000

8.75

7.5

10.0

8.75

TOTAL

313,000
-

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.

+
+

The operator of YaYa Gas-n-Grub wants to allocate gasoline delivery in such a way that the costs to franchise owners are minimized.

Model 1: Minimize total delivery cost#

-

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 \(s\). -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:

+

The first optimization model aims to minimize the total cost of delivery to all franchise owners.

+

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 \(s\).

+

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.

+

In mathematical terms, we can write the full problem as

\[\begin{split} \begin{align*} \min \quad & \sum_{d=1}^{n_d} \sum_{s=1}^{n_s} r_{d, s} x_{d, s} \\ - \text{s.t.} \quad &\sum_{s=1}^{n_s} x_{d, s} = D_d & \forall \, d\in 1, \dots, n_d & \quad \text{(demand constraints)}\\ - & \sum_{d=1}^{n_d} x_{d, s} \leq S_s & \forall \, s\in 1, \dots, n_s & \quad \text{(supply constraints)} + \text{s.t.} \quad &\sum_{s=1}^{n_s} x_{d, s} = D_d & \forall \, d = 1, \dots, n_d & \quad \text{(demand constraints)}\\ + & \sum_{d=1}^{n_d} x_{d, s} \leq S_s & \forall \, s = 1, \dots, n_s & \quad \text{(supply constraints)}\\ + & x_{d, s} \geq 0 & \forall \, d = 1, \dots, n_d, \, s = 1, \dots, n_s. \end{align*} \end{split}\]
@@ -704,7 +706,7 @@

Data Entrydisplay(HTML("<br><b>Gasoline Demand (Gallons)</b>")) display(demand.to_frame()) -display(HTML("<br><b>Transportation Rates (€ cents per Gallon)</b>")) +display(HTML("<br><b>Transportation Rates ($ cents per Gallon)</b>")) display(rates)

@@ -802,7 +804,7 @@

Data Entry
Transportation Rates (€ cents per Gallon)
+

Transportation Rates ($ cents per Gallon)