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Branch-and-cut for mixed-integer robust chance-constrained optimization with discrete distributions

  • We study robust chance-constrained problems with mixed-integer design variables and ambiguity sets consisting of discrete probability distributions. Allowing general non-convex constraint functions, we develop a branch-and-cut framework using scenario-based cutting planes to generate lower bounds. The cutting planes are obtained by exploiting the classical big-M reformulation of the chance-constrained problem in the case of discrete distributions. Furthermore, we include the calculation of initial feasible solutions based on a bundle method applied to an approximation of the original problem into the branch-and-cut procedure. We conclude with a detailed discussion about the practical performance of the branch-and-cut framework with and without initial feasible solutions. In our experiments we focus on gas transport problems under uncertainty and provide a comparison of our method with solving the classical reformulation directly for various real-world sized instances.

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Metadaten
Author:Daniela Bernhard, Frauke Liers, Michael Stingl
Document Type:Preprint
Language:English
Date of Publication (online):2025/10/28
Release Date:2025/10/28
Subprojects:B06
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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