Solving General Ranking and Selection Problems with Risk-aversion - Université d'Évry
Conference Papers Year : 2023

Solving General Ranking and Selection Problems with Risk-aversion

Abstract

In simulation optimization, a ranking and selection (R&S) problem aims to select the best from candidate solutions, subject to a limited budget of simulation runs. Existing R&S literature focuses on selecting the best solution, based on a ranking criterion defined by the mean performance. Ignoring performance variance in the ranking criterion definition, however, may lead to selecting a very risky solution, with low average performance but high variation. In this paper, we address a new risk-averse R&S problem, which is a generalization of the classic (risk-neutral) R&S problem, by ranking the solutions via the weighted sum of the mean and variance of the performance. For this novel problem, a new approach is developed based on Karush-Kuhn-Tucker conditions, which is a generalization of optimal computing budget allocation (OCBA). Numerical experiments are conducted to show its efficiency.
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Dates and versions

hal-04361023 , version 1 (22-12-2023)

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Cite

Ming Liu, Yecheng Zhao, Feng Chu, Mengchu Zhou, Zhongzheng Liu. Solving General Ranking and Selection Problems with Risk-aversion. 20th IEEE International Conference on Networking, Sensing and Control (ICNSC 2023), Oct 2023, Marseille, France. pp.1-6, ⟨10.1109/ICNSC58704.2023.10319048⟩. ⟨hal-04361023⟩
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