Robust min-max (regret) optimization using ordered weighted averaging

Werner Baak, Marc Goerigk, Adam Kasperski and Paweł Zieliński

European Journal of Operational Research, 2025, vol. 322, issue 1, 171-181

Abstract: In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel variant of ordered weighted averaging (OWA) for optimization problems. It generalizes the classic OWA approach, which includes the robust min–max optimization as a special case, as well as the min–max regret optimization. We derive new complexity results for this setting, including insights into the inapproximability and approximability of this problem. In particular, we provide stronger positive approximation results that asymptotically improve the previously best-known bounds for the classic OWA approach.

Keywords: Robust optimization; Ordered weighted averaging; Min–max regret (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:322:y:2025:i:1:p:171-181

DOI: 10.1016/j.ejor.2024.10.028

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