Solving General Ranking and Selection Problems with Risk-aversion
Document Type
Conference Proceeding
Publication Date
1-1-2023
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.
Identifier
85179628874 (Scopus)
ISBN
[9798350369502]
Publication Title
Icnsc 2023 20th IEEE International Conference on Networking Sensing and Control
External Full Text Location
https://doi.org/10.1109/ICNSC58704.2023.10319048
Grant
71771048
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Liu, Ming; Zhao, Yecheng; Chu, Feng; Zhou, Mengchu; and Liu, Zhongzheng, "Solving General Ranking and Selection Problems with Risk-aversion" (2023). Faculty Publications. 2332.
https://digitalcommons.njit.edu/fac_pubs/2332