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

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  • Baak, Werner
  • Goerigk, Marc
  • Kasperski, Adam
  • Zieliński, Paweł

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.

Suggested Citation

  • Baak, Werner & Goerigk, Marc & Kasperski, Adam & Zieliński, Paweł, 2025. "Robust min-max (regret) optimization using ordered weighted averaging," European Journal of Operational Research, Elsevier, vol. 322(1), pages 171-181.
    • Handle: RePEc:eee:ejores:v:322:y:2025:i:1:p:171-181
    DOI: 10.1016/j.ejor.2024.10.028
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    References listed on IDEAS

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    1. Vittorio Bilò & Ioannis Caragiannis & Angelo Fanelli & Michele Flammini & Gianpiero Monaco, 2017. "Simple Greedy Algorithms for Fundamental Multidimensional Graph Problems," Post-Print hal-02089412, HAL.
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