Risk-Averse Regret Minimization in Multistage Stochastic Programs

• 成果类型: Article
• 署名作者: Poursoltani, Mehran; Delage, Erick; Georghiou, Angelos
• 署名单位: Universite de Montreal; HEC Montreal; Universite de Montreal; HEC Montreal; University of Cyprus
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2429
• 发表日期: 2024
• 页码: 1727-1738
• 关键词: optimization problems; robust optimization; relative robust; minmax regret; MODEL; max

摘要

Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing regret. In a multistage stochastic programming setting with a discrete probability distribution, we explore the idea of risk-averse regret minimization, where the benchmark policy can only benefit from foreseeing increment steps into the future. The increment -regret model naturally interpolates between the popular ex ante and ex post regret models. We provide theoretical and numerical insights about this family of models under popular coherent risk measures and shed new light on the conservatism of the increment -regret minimizing solutions.