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Pyomo.GDP: Disjunctive Models in Python

Computer Aided Chemical Engineering

Chen, Qi; Johnson, Emma S.; Siirola, John D.; Grossmann, Ignacio E.

In this work, we describe new capabilities for the Pyomo.GDP modeling environment, moving beyond classical reformulation approaches to include non-standard reformulations and a new logic-based solver, GDPopt. Generalized Disjunctive Programs (GDPs) address optimization problems involving both discrete and continuous decision variables. For difficult problems, advanced reformulations such as the disjunctive “basic step” to intersect multiple disjunctions or the use of procedural reformulations may be necessary. Complex nonlinear GDP models may also be tackled using logic-based outer approximation. These expanded capabilities highlight the flexibility that Pyomo.GDP offers modelers in applying novel strategies to solve difficult optimization problems.

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