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arXiv · 2609.29478

Exact Methods for Solving k-Delete Recoverable Robust 0-1 Problems Under Budgeted Uncertainty

Yasmine Beck·Christina Büsing·Ivana Ljubić
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Abstract

We study the k-delete recoverable robust 0-1 problem in which a decision-maker solves a combinatorial optimization problem subject to objective uncertainty. The model follows a two-stage robust setup. The decision-maker first commits to an initial plan and may then revoke up to k components of this decision after the uncertainty is revealed. The underlying uncertainty is modeled using a budgeted uncertainty set so that the decision-maker only hedges against a limited number of deviations in the uncertain parameters. We present four reformulations of the k-delete recoverable robust problem, which can be tackled using (i) general-purpose mixed-integer linear programming solvers, (ii) branch-and-cut methods, or (iii) column-and-constraint generation algorithms. For each formulation, we identify suitable solution methods and prove their correctness. Overall, we present eight approaches to solve the k-delete recoverable robust problem, which we assess and compare in an extensive computational study on instances of the assignment problem and the single-source capacitated facility location problem.

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Yasmine Beck, Christina Büsing, Ivana Ljubić. 2026-08-24. Exact Methods for Solving k-Delete Recoverable Robust 0-1 Problems Under Budgeted Uncertainty. https://arxiv.org/abs/2609.29478

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