Disjunctive programming model for the synthesis of property-based water supply network with multiple resources

Zhou, Wenjing; Iqbal, Kashif; Sun, Xiaohan; Gan, Dinghui; Deng, Chun; Ponce-Ortega, José María; Chen, Chunmao

doi:10.1016/j.cherd.2022.08.027

Cited by 3 publications

(1 citation statement)

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“…Within the optimization community, there is a high volume of ongoing research that relies on GDP to formulate models for a variety of applications. Due to the combinatorial nature of system design problems, the GDP paradigm has been applied to the synthesis of complex processes and networks [21,27], the planning and optimal control of energy systems [9], and the modeling of chemical synthesis under uncertainty [8]. These and numerous other applications of GDP illustrate the benefit of having a robust package for GDP that removes much of the overhead associated with developing and testing GDP models.…”

Section: Introductionmentioning

confidence: 99%

DisjunctiveProgramming.jl: Generalized Disjunctive Programming Models and Algorithms for JuMP

2023

JCON

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We present a Julia package, DisjunctiveProgramming.jl, that extends the functionality in JuMP.jl to allow modeling problems via logical propositions and disjunctive constraints. Such models can then be reformulated into Mixed-Integer Programs (MIPs) that can be solved with the various MIP solvers supported by JuMP. To do so, logical propositions are converted to Conjunctive Normal Form (CNF) and reformulated into equivalent algebraic constraints. Disjunctions are reformulated into mixed-integer constraints via the reformulation technique specified by the user (Big-M or Hull reformulations). The package supports reformulations for disjunctions containing linear, quadratic, and nonlinear constraints.

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