Optimization Online


Risk-averse Regret Minimization in Multi-stage Stochastic Programs

Mehran Poursoltani(mehran.poursoltani***at***hec.ca)
Erick Delage(erick.delage***at***hec.ca)
Angelos Georghiou(georghiou.angelos***at***ucy.ac.cy)

Abstract: 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 multi-stage 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 Delta steps into the future. The Delta-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 Delta-regret minimizing solutions.

Keywords: Regret minimization, risk measures, multi-stage stochastic programming, robust optimization

Category 1: Stochastic Programming

Category 2: Robust Optimization

Category 3: Applications -- OR and Management Sciences


Download: [PDF]

Entry Submitted: 12/11/2021
Entry Accepted: 12/11/2021
Entry Last Modified: 12/11/2021

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society