TY - GEN
T1 - Solving General Ranking and Selection Problems with Risk-aversion
AU - Liu, Ming
AU - Zhao, Yecheng
AU - Chu, Feng
AU - Zhou, Mengchu
AU - Liu, Zhongzheng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Optimal computing budget allocation
KW - Ranking and selection
KW - Risk-averse
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85179628874&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179628874&partnerID=8YFLogxK
U2 - 10.1109/ICNSC58704.2023.10319048
DO - 10.1109/ICNSC58704.2023.10319048
M3 - Conference contribution
AN - SCOPUS:85179628874
T3 - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
BT - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023
Y2 - 25 October 2023 through 27 October 2023
ER -