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In robust optimization, the impact of uncertainty is typically modeled using the min-max approach, where the goal is to optimize the solution cost under a worst-case scenario. This approach does not take into account good scenarios that can occur and may lead to very conservative solutions. The R* uninorm is an aggregation operator that selects the minimal value when all arguments are below a selected threshold and the maximal value otherwise. Its axiomatic characterization, applications to decision-making, and comparison to the traditional Hurwicz criterion were described by Fargier and Guillaume in 2020. The goal of this paper is to present an application of the R* approach to selected optimization problems under uncertainty.

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Using the R* Criterion to Selected Optimization Problems Under Uncertainty

  • 会议论文

  • 发表日期：

    2025年

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  • Romain Guillaume,
  • Adam Kasperski,
  • Szymon Wróbel,
  • Paweł Zieliński

摘要

In robust optimization, the impact of uncertainty is typically modeled using the min-max approach, where the goal is to optimize the solution cost under a worst-case scenario. This approach does not take into account good scenarios that can occur and may lead to very conservative solutions. The R* uninorm is an aggregation operator that selects the minimal value when all arguments are below a selected threshold and the maximal value otherwise. Its axiomatic characterization, applications to decision-making, and comparison to the traditional Hurwicz criterion were described by Fargier and Guillaume in 2020. The goal of this paper is to present an application of the R* approach to selected optimization problems under uncertainty.