SECQUOIA · ZedongPeng · Aug 3, 2024 · Aug 3, 2024 · Aug 3, 2024 · Aug 18, 2024
diff --git a/.gitignore b/.gitignore
@@ -132,3 +132,4 @@ dmypy.json
 .idea/
 
 gdplib/*/benchmark_result/
+gdplib/*/model_size_report.md
diff --git a/gdplib/batch_processing/README.md b/gdplib/batch_processing/README.md
@@ -0,0 +1,27 @@
+# Batch Processing Optimization Problem
+
+The model is designed to minimize the total cost associated with the design and operation of a plant consisting of multiple parallel processing units with intermediate storage tanks. It involves determining the optimal number and sizes of processing units, batch sizes for different products at various stages, and sizes and placements of storage tanks to ensure operational efficiency while meeting production requirements within a specified time horizon.
+
+## Problem Details
+### Optimal Solution
+
+TODO
+
+### Size
+
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |      288 |
+| Binary variables      |      138 |
+| Integer variables     |        0 |
+| Continuous variables  |      150 |
+| Disjunctions          |        9 |
+| Disjuncts             |       18 |
+| Constraints           |      601 |
+| Nonlinear constraints |        1 |
+
+
+## References
+> [1] Ravemark, E. Optimization models for design and operation of chemical batch processes. Ph.D. Thesis, ETH Zurich, 1995. https://doi.org/10.3929/ethz-a-001591449
+> 
+> [2] Vecchietti, A., & Grossmann, I. E. (1999). LOGMIP: a disjunctive 0–1 non-linear optimizer for process system models. Computers & chemical engineering, 23(4-5), 555-565. https://doi.org/10.1016/S0098-1354(97)87539-4
diff --git a/gdplib/biofuel/README.md b/gdplib/biofuel/README.md
@@ -17,12 +17,13 @@ Adapted from:
 Optimal objective value: 4067
 
 ### Size
-- Variables: 36804
-    - Boolean: 516
-    - Binary: 0
-    - Integer: 4320
-    - Continuous: 31968
-- Constraints: 12884
-    - Nonlinear: 12
-- Disjuncts: 516
-- Disjunctions: 252
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |    36840 |
+| Binary variables      |      516 |
+| Integer variables     |     4356 |
+| Continuous variables  |    31968 |
+| Disjunctions          |      252 |
+| Disjuncts             |      516 |
+| Constraints           |    12884 |
+| Nonlinear constraints |       12 |
diff --git a/gdplib/cstr/README.md b/gdplib/cstr/README.md
@@ -7,24 +7,23 @@ The optimal solution should yield NT reactors with a recycle before reactor NT.
 Reference:
 > Linan, D. A., Bernal, D. E., Gomez, J. M., & Ricardez-Sandoval, L. A. (2021). Optimal synthesis and design of catalytic distillation columns: A rate-based modeling approach. Chemical Engineering Science, 231, 116294. https://doi.org/10.1016/j.ces.2020.116294
 
-### Solution
+## Problem Details
+
+### Optimal Solution
 
 Best known objective value: 3.06181298849707
 
 ### Size
 
 Number of reactors in series is 5.
 
-| Problem   | vars | Bool | bin | int | cont | cons | nl | disj | disjtn |
-|-----------|------|------|-----|-----|------|------|----|------|--------|
-| gdp_reactors | 71 | 15 | 0 | 0 | 56 | 25 | 2 | 20 | 10 |
-
-- ``vars``: variables
-- ``Bool``: Boolean variables
-- ``bin``: binary variables
-- ``int``: integer variables
-- ``cont``: continuous variables
-- ``cons``: constraints
-- ``nl``: nonlinear constraints
-- ``disj``: disjuncts
-- ``disjtn``: disjunctions
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |       76 |
+| Binary variables      |       20 |
+| Integer variables     |        0 |
+| Continuous variables  |       56 |
+| Disjunctions          |       10 |
+| Disjuncts             |       20 |
+| Constraints           |      100 |
+| Nonlinear constraints |       17 |
diff --git a/gdplib/disease_model/README.md b/gdplib/disease_model/README.md
@@ -0,0 +1,21 @@
+# SIR Disease Model
+This is the SIR disease model using a low/high transmission parameter. The model simulates the spread of an infectious disease over a series of bi-weekly periods, using a disjunctive programming approach to account for variations in disease transmission rates.
+
+## Problem details
+
+### Optimal Solution
+
+### Size
+
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |     1250 |
+| Binary variables      |       52 |
+| Integer variables     |        0 |
+| Continuous variables  |     1198 |
+| Disjunctions          |       26 |
+| Disjuncts             |       52 |
+| Constraints           |      831 |
+| Nonlinear constraints |        0 |
+
+## References
diff --git a/gdplib/ex1_linan_2023/README.md b/gdplib/ex1_linan_2023/README.md
@@ -1,4 +1,4 @@
-## Example 1 Problem of Liñán (2023)
+# Example 1 Problem of Liñán (2023)
 
 The Example 1 Problem of Liñán (2023) is a simple optimization problem that involves two Boolean variables, two continuous variables, and a nonlinear objective function. The problem is formulated as a Generalized Disjunctive Programming (GDP) model.
 
@@ -8,11 +8,23 @@ The objective function is `-0.9995999999999999` when the continuous variables ar
 
 The objective function originates from Problem No. 6 of Gomez's paper, and Liñán introduced logical propositions, logical disjunctions, and the following equations as constraints.
 
-### References
+## Problem details
+
+### Size
+
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |       12 |
+| Binary variables      |       10 |
+| Integer variables     |        0 |
+| Continuous variables  |        2 |
+| Disjunctions          |        2 |
+| Disjuncts             |       10 |
+| Constraints           |       10 |
+| Nonlinear constraints |        0 |
+
+## References
 
 > [1] Liñán, D. A., & Ricardez-Sandoval, L. A. (2023). A Benders decomposition framework for the optimization of disjunctive superstructures with ordered discrete decisions. AIChE Journal, 69(5), e18008. https://doi.org/10.1002/aic.18008
 >
 > [2] Gomez, S., & Levy, A. V. (1982). The tunnelling method for solving the constrained global optimization problem with several non-connected feasible regions. In Numerical Analysis: Proceedings of the Third IIMAS Workshop Held at Cocoyoc, Mexico, January 1981 (pp. 34-47). Springer Berlin Heidelberg. https://doi.org/10.1007/BFb0092958
-
-
----
diff --git a/gdplib/gdp_col/README.md b/gdplib/gdp_col/README.md
@@ -14,16 +14,13 @@ Best known objective value: 19,430
 
 ### Size
 
-| Problem   | vars | Bool | bin | int | cont | cons | nl | disj | disjtn |
-|-----------|------|------|-----|-----|------|------|----|------|--------|
-| gdp_col | 433 | 28 | 0 | 0 | 405 | 603 | 255 | 28 | 15 |
-
-- ``vars``: variables
-- ``Bool``: Boolean variables
-- ``bin``: binary variables
-- ``int``: integer variables
-- ``cont``: continuous variables
-- ``cons``: constraints
-- ``nl``: nonlinear constraints
-- ``disj``: disjuncts
-- ``disjtn``: disjunctions
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |      442 |
+| Binary variables      |       30 |
+| Integer variables     |        0 |
+| Continuous variables  |      412 |
+| Disjunctions          |       15 |
+| Disjuncts             |       30 |
+| Constraints           |      610 |
+| Nonlinear constraints |      262 |
diff --git a/gdplib/hda/README.md b/gdplib/hda/README.md
@@ -12,23 +12,20 @@ The MINLP formulation of this problem is available in GAMS https://www.gams.com/
 This model was reimplemented by Yunshan Liu @Yunshan-Liu .
 
 ## Problem Details
-### Solution
+### Optimal Solution
 
 Best known objective value: 5801.27
 
 
 ### Size
 
-| Problem   | vars | Bool | bin | int | cont | cons | nl | disj | disjtn |
-|-----------|------|------|-----|-----|------|------|----|------|--------|
-| HDA Model | 721 | 12 | 0 | 0 | 709 | 728 | 151 | 12 | 6 |
-
-- ``vars``: variables
-- ``Bool``: Boolean variables
-- ``bin``: binary variables
-- ``int``: integer variables
-- ``cont``: continuous variables
-- ``cons``: constraints
-- ``nl``: nonlinear constraints
-- ``disj``: disjuncts
-- ``disjtn``: disjunctions
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |     1158 |
+| Binary variables      |       12 |
+| Integer variables     |        0 |
+| Continuous variables  |     1146 |
+| Disjunctions          |        6 |
+| Disjuncts             |       12 |
+| Constraints           |      728 |
+| Nonlinear constraints |      151 |
diff --git a/gdplib/jobshop/README.md b/gdplib/jobshop/README.md
@@ -0,0 +1,25 @@
+# GDP Distillation Column Design
+
+This model solves a jobshop scheduling, which has a set of jobs which must be processed in sequence of stages but not all jobs require all stages. A zero wait transfer policy is assumed between stages. To obtain a feasible solution it is necessary to eliminate all clashes between jobs. It requires that no two jobs be performed at any stage at any time. The objective is to minimize the makespan, the time to complete all jobs.
+
+## Problem Details
+
+### Optimal Solution
+
+### Size
+
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |       10 |
+| Binary variables      |        6 |
+| Integer variables     |        0 |
+| Continuous variables  |        4 |
+| Disjunctions          |        3 |
+| Disjuncts             |        6 |
+| Constraints           |        9 |
+| Nonlinear constraints |        0 |
+
+## References
+
+> [1] Raman & Grossmann, Modelling and computational techniques for logic based integer programming, Computers and Chemical Engineering 18, 7, p.563-578, 1994. DOI: 10.1016/0098-1354(93)E0010-7.
+> [2] Aldo Vecchietti, LogMIP User's Manual, http://www.logmip.ceride.gov.ar/, 2007
diff --git a/gdplib/kaibel/README.md b/gdplib/kaibel/README.md
@@ -15,19 +15,13 @@ Best known objective value: 99,709
 
 ### Size
 
-| Problem   | vars | Bool | bin | int | cont | cons | nl | disj | disjtn |
-|-----------|------|------|-----|-----|------|------|----|------|--------|
-| Kaibel Column | 3605 | 178 | 0 | 0 | 3427 | 5715 | 2124 | 182 | 100 |
-
-- ``vars``: variables
-- ``Bool``: Boolean variables
-- ``bin``: binary variables
-- ``int``: integer variables
-- ``cont``: continuous variables
-- ``cons``: constraints
-- ``nl``: nonlinear constraints
-- ``disj``: disjuncts
-- ``disjtn``: disjunctions
-
-
-
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |     4033 |
+| Binary variables      |      200 |
+| Integer variables     |        0 |
+| Continuous variables  |     3833 |
+| Disjunctions          |      100 |
+| Disjuncts             |      200 |
+| Constraints           |     5790 |
+| Nonlinear constraints |     2128 |
diff --git a/gdplib/med_term_purchasing/README.md b/gdplib/med_term_purchasing/README.md
@@ -0,0 +1,32 @@
+# Medium-term Purchasing Contracts Problem
+
+Medium-term Purchasing Contracts problem from https://www.minlp.org/library/problem/index.php?i=129
+
+This model maximizes profit in a short-term horizon in which various contracts are available for purchasing raw materials. The model decides inventory levels, amounts to purchase, amount sold, and flows through the process nodes while maximizing profit. The four different contracts available are:
+1. **FIXED PRICE CONTRACT**: buy as much as you want at constant price 
+2. **DISCOUNT CONTRACT**: quantities below minimum amount cost RegPrice. Any additional quantity above min amount costs DiscoutPrice.
+3. **BULK CONTRACT**: If more than min amount is purchased, whole purchase is at discount price.
+4. **FIXED DURATION CONTRACT**: Depending on length of time contract is valid, there is a purchase price during that time and min quantity that must be purchased
+
+## Problem Details
+
+### Solution
+
+
+### Size
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |     1165 |
+| Binary variables      |      216 |
+| Integer variables     |        0 |
+| Continuous variables  |      949 |
+| Disjunctions          |       72 |
+| Disjuncts             |      216 |
+| Constraints           |      762 |
+| Nonlinear constraints |        0 |
+
+
+## References
+> [1] Vecchietti, A., & Grossmann, I. (2004). Computational experience with logmip solving linear and nonlinear disjunctive programming problems. Proc. of FOCAPD, 587-590.
+> 
+> [2] Park, M., Park, S., Mele, F. D., & Grossmann, I. E. (2006). Modeling of purchase and sales contracts in supply chain optimization. Industrial and Engineering Chemistry Research, 45(14), 5013-5026. DOI: 10.1021/ie0513144
diff --git a/gdplib/methanol/README.md b/gdplib/methanol/README.md
@@ -15,19 +15,13 @@ Best known objective value: 1583.00
 
 ### Size
 
-| Problem   | vars | Bool | bin | int | cont | cons | nl | disj | disjtn |
-|-----------|------|------|-----|-----|------|------|----|------|--------|
-| Methanol Production | 285 | 8 | 0 | 0 | 277 | 429 | 55 | 8 | 4 |
-
-- ``vars``: variables
-- ``Bool``: Boolean variables
-- ``bin``: binary variables
-- ``int``: integer variables
-- ``cont``: continuous variables
-- ``cons``: constraints
-- ``nl``: nonlinear constraints
-- ``disj``: disjuncts
-- ``disjtn``: disjunctions
-
-
-
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |      287 |
+| Binary variables      |        8 |
+| Integer variables     |        0 |
+| Continuous variables  |      279 |
+| Disjunctions          |        4 |
+| Disjuncts             |        8 |
+| Constraints           |      429 |
+| Nonlinear constraints |       55 |
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Expand Up		@@ -132,3 +132,4 @@ dmypy.json
		.idea/

		gdplib/*/benchmark_result/
		gdplib/*/model_size_report.md