Merge pull request #54 from SECQUOIA/LDSDA_init

Fix directory and documentation issues

Author: ZedongPeng

gdplib/__init__.py

@@ -13,3 +13,6 @@
 import gdplib.disease_model
 import gdplib.med_term_purchasing
 import gdplib.syngas
+import gdplib.ex1_linan_2023
+import gdplib.small_batch
+import gdplib.cstr

gdplib/cstr/__init__.py

@@ -0,0 +1,3 @@
+from .gdp_reactor import build_model
+
+__all__ = ['build_model']

gdplib/cstr/gdp_reactor.py

@@ -17,7 +17,7 @@
 from pyomo.opt.base.solvers import SolverFactory
 
 
-def build_cstrs(NT: int = 5) -> pyo.ConcreteModel():
+def build_model(NT: int = 5) -> pyo.ConcreteModel():
     """
     Build the CSTR superstructure model of size NT.
     NT is the number of reactors in series.
@@ -915,7 +915,7 @@ def obj_rule(m):
 
 
 if __name__ == "__main__":
-    m = build_cstrs()
+    m = build_model()
     pyo.TransformationFactory("core.logical_to_linear").apply_to(m)
     pyo.TransformationFactory("gdp.bigm").apply_to(m)
     pyo.SolverFactory("gams").solve(

gdplib/ ex1_linan_2023/README.md gdplib/ex1_linan_2023/README.mdgdplib/ ex1_linan_2023/README.md renamed to gdplib/ex1_linan_2023/README.md

@@ -4,11 +4,15 @@ The Example 1 Problem of Liñán (2023) is a simple optimization problem that in
 
 The Boolean variables are associated with disjuncts that define the feasible regions of the continuous variables. The problem also includes logical constraints that ensure that only one Boolean variable is true at a time. Additionally, there are two disjunctions, one for each Boolean variable, where only one disjunct in each disjunction must be true. A specific logical constraint also enforces that `Y1[3]` must be false, making this particular disjunct infeasible.
 
-The objective function is -0.9995999999999999 when the continuous variables are alpha = 0 (`Y1[2]=True`) and beta=-0.7 (`Y2[3]=True`).
+The objective function is `-0.9995999999999999` when the continuous variables are alpha = 0 (`Y1[2]=True `) and beta=-0.7 (`Y2[3]=True`).
 
 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
 
-[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
+> [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/ex1_linan_2023/__init__.py

@@ -0,0 +1,3 @@
+from .ex1_linan_2023 import build_model
+
+__all__ = ['build_model']

gdplib/ ex1_linan_2023/ ex1_linan_2023.py gdplib/ex1_linan_2023/ex1_linan_2023.pygdplib/ ex1_linan_2023/ ex1_linan_2023.py renamed to gdplib/ex1_linan_2023/ex1_linan_2023.py

@@ -1,7 +1,7 @@
 """
 ex1_linan_2023.py: Toy problem from Liñán and Ricardez-Sandoval (2023) [1]
 
-TThe ex1_linan.py file is a simple optimization problem that involves two Boolean variables, two continuous variables, and a nonlinear objective function.
+The ex1_linan.py file 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. 
 The Boolean variables are associated with disjuncts that define the feasible regions of the continuous variables. 
 The problem includes logical constraints that ensure that only one Boolean variable is true at a time.
@@ -12,6 +12,7 @@
 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

@@ -1,4 +1,4 @@
-## gdp_small_batch.py
+## 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.
 
@@ -10,6 +10,6 @@ The solution is 167427.65711.
 
 ### 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
-
-[2] Ovalle, D., Liñán, D. A., Lee, A., Gómez, J. M., Ricardez-Sandoval, L., Grossmann, I. E., & Bernal Neira, D. E. (2024). Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs. arXiv preprint arXiv:2405.05358. https://doi.org/10.48550/arXiv.2405.05358
+> [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
+>
+> [2] Ovalle, D., Liñán, D. A., Lee, A., Gómez, J. M., Ricardez-Sandoval, L., Grossmann, I. E., & Bernal Neira, D. E. (2024). Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs. arXiv preprint arXiv:2405.05358. https://doi.org/10.48550/arXiv.2405.05358

gdplib/small_batch/__init__.py

@@ -0,0 +1,3 @@
+from .gdp_small_batch import build_model
+
+__all__ = ['build_model']

gdplib/small_batch/gdp_small_batch.py

@@ -7,7 +7,9 @@
 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
+
 [2] Ovalle, D., Liñán, D. A., Lee, A., Gómez, J. M., Ricardez-Sandoval, L., Grossmann, I. E., & Bernal Neira, D. E. (2024). Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs. arXiv preprint arXiv:2405.05358. https://doi.org/10.48550/arXiv.2405.05358
+
 """
 
 import os
@@ -20,7 +22,7 @@
 from pyomo.opt.base.solvers import SolverFactory
 
 
-def build_small_batch():
+def build_model():
     """
     Build the GDP model for the small batch problem.
 
@@ -32,6 +34,7 @@ def build_small_batch():
     References
     ----------
     [1] Kocis, G. R.; Grossmann, I. E. (1988). Global Optimization of Nonconvex Mixed-Integer Nonlinear Programming (MINLP) Problems in Process Synthesis. Ind. Eng. Chem. Res., 27(8), 1407-1421. https://doi.org/10.1021/ie00080a013
+
     [2] Ovalle, D., Liñán, D. A., Lee, A., Gómez, J. M., Ricardez-Sandoval, L., Grossmann, I. E., & Neira, D. E. B. (2024). Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs. arXiv preprint arXiv:2405.05358. https://doi.org/10.48550/arXiv.2405.05358
     """
     NK = 3
@@ -426,7 +429,7 @@ def obj_rule(m):
 
 
 if __name__ == "__main__":
-    m = build_small_batch()
+    m = build_model()
     pyo.TransformationFactory("core.logical_to_linear").apply_to(m)
     pyo.TransformationFactory("gdp.bigm").apply_to(m)
     pyo.SolverFactory("gams").solve(