update size table

gdplib/cstr/README.md

@@ -7,6 +7,8 @@ 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
 
+## Problem Details
+
 ### Optimal Solution
 
 Best known objective value: 3.06181298849707

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
-
-
----

gdplib/small_batch/README.md

@@ -1,14 +1,31 @@
-## Small Batch Scheduling Problem
+# Small Batch Scheduling Problem
 
 The gdp_small_batch.py module contains the GDP model for the small batch problem based on the Kocis and Grossmann (1988) paper.
 
 The problem is based on the Example 4 of the paper.
 
 The objective is to minimize the investment cost of the batch units.
 
-The solution is 167427.65711.
+## Problem Details
 
-### References
+### Optimal Solution
+
+The optimal solution is `167427.65711`.
+
+### Size
+
+| Component             |   Number |
+|:----------------------|---------:|
+| Variables             |       37 |
+| Binary variables      |       18 |
+| Integer variables     |        0 |
+| Continuous variables  |       19 |
+| Disjunctions          |        9 |
+| Disjuncts             |       18 |
+| Constraints           |       34 |
+| Nonlinear constraints |        1 |
+
+## References
 
 > [1] Kocis, G. R.; Grossmann, I. E. Global Optimization of Nonconvex Mixed-Integer Nonlinear Programming (MINLP) Problems in Process Synthesis. Ind. Eng. Chem. Res. 1988, 27 (8), 1407-1421. https://doi.org/10.1021/ie00080a013
 >

generate_model_size_report.py

@@ -10,6 +10,7 @@
         "biofuel": [],
         "cstr": [],
         "disease_model": [],
+        "ex1_linan_2023": [],
         "gdp_col": [],
         "hda": [],
         "jobshop": [],
@@ -27,6 +28,7 @@
         ],
         "modprodnet": ["Growth", "Dip", "Decay", "Distributed", "QuarterDistributed"],
         "positioning": [],
+        "small_batch": [],
         "spectralog": [],
         "stranded_gas": [],
         "syngas": [],