Pareto Set Selection for Multiobjective Stochastic Simulation Model

Seon Han Choi, Tag Gon Kim

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Article number8400565
Pages (from-to)4256-4269
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number11
StatePublished - Nov 2020


  • Multiobjective optimization
  • Pareto optimality
  • ranking and selection (RandS)
  • statistical hypothesis test
  • stochastic simulation


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