Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test

Seon Han Choi, Tag Gon Kim

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

This paper proposes an efficient ranking and selection algorithm for a stochastic simulation model. The proposed algorithm evaluates an uncertainty to assess whether the observed best design is truly optimal, based on hypothesis test. Then, it conservatively allocates additional simulation resources to reduce uncertainty with an intuitive allocation rule in each iteration of a sequential procedure. This conservative allocation provides a high robustness to noise for the algorithm. The results of several experiments demonstrated its improved performance compared to the other algorithms in the literature. The algorithm can be an efficient way to solve optimization problems in real-world systems where significant noise exists.

Original languageEnglish
Article number7883950
Pages (from-to)1555-1565
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number9
DOIs
StatePublished - Sep 2018

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • High robustness to noise
  • ranking and selection (R&S)
  • statistical hypothesis test
  • stochastic simulation model

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