# Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging

arXiv: https://arxiv.org/abs/2308.08522 · HTML: https://mlsanity.com/paper/2308.08522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review](https://mlsanity.com/paper/2608.19214/) (2026)

## Abstract (arXiv)

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 robust min-max optimization as a special case, as well as 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. In computational experiments, we evaluate the quality of the proposed methods and compare the proposed setting with classic OWA and min-max regret approaches.