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

Bernhard D, Liers F, Stingl M (2026)

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Publication Type: Journal article

Publication year: 2026

Journal

DOI: 10.1016/j.ejor.2026.08.027

Abstract

We study robust chance-constrained problems with mixed-integer design variables and ambiguity sets consisting of discrete probability distributions. Allowing some classes of 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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How to cite

APA:

Bernhard, D., Liers, F., & Stingl, M. (2026). Branch-and-cut for mixed-integer robust chance-constrained optimization with discrete distributions. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2026.08.027

MLA:

Bernhard, Daniela, Frauke Liers, and Michael Stingl. "Branch-and-cut for mixed-integer robust chance-constrained optimization with discrete distributions." European Journal of Operational Research (2026).

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