Pareto Set Selection for Multiobjective Stochastic Simulation Model
- 주제(키워드) Uncertainty , System analysis and design , Resource management , Nickel , Stochastic processes , Computational modeling , Robustness , Multiobjective optimization , Pareto optimality , ranking and selection (R&S) , statistical hypothesis test , stochastic simulation
- 주제(기타) Automation & Control Systems, Computer Science
- 설명문(URI) https://www.webofscience.com/wos/woscc/full-record/WOS:000578826300028
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2020
- URI http://www.dcollection.net/handler/ewha/000000185409
- 본문언어 영어
- Published As https://doi.org/10.1109/TSMC.2018.2846680
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
This paper addresses the problem of selecting a Pareto set from among finite alternatives, where each alternative has multiple performance measures evaluated by stochastic simulations. Under limited simulation resources, we propose an efficient algorithm for solving this problem based on a statistical hypothesis test. Using the test, the proposed algorithm evaluates the uncertainty of each design based on the observed simulation results to identify whether the selected Pareto set is accurate. Based on the evaluated uncertainty, the algorithm assigns additional resources to the designs to maximize the accuracy of the selected Pareto set. Applying the sequential procedure, the algorithm increases the precision of the observed information selectively and gradually. Several experiments, including a practical case study, demonstrated its improved efficiency compared to the existing algorithms in the literature. This improved efficiency, along with low complexity and high robustness to noise, allows the proposed algorithm to be effectively applied to practical system designs.
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