Bernhard D, Liers-Bergmann F, Stingl M (2026)
Publication Status: Submitted
Publication Type: Unpublished / Preprint
Future Publication Type: Journal article
Publication year: 2026
URI: https://opus4.kobv.de/opus4-trr154/609
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.
APA:
Bernhard, D., Liers-Bergmann, F., & Stingl, M. (2026). Branch-and-cut for mixed-integer robust chance-constrained optimization with discrete distributions. (Unpublished, Submitted).
MLA:
Bernhard, Daniela, Frauke Liers-Bergmann, and Michael Stingl. Branch-and-cut for mixed-integer robust chance-constrained optimization with discrete distributions. Unpublished, Submitted. 2026.
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