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Fully sequential procedures for comparing constrained systems via simulation

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  • Sigrún Andradóttir
  • Seong‐Hee Kim

Abstract

We consider the problem of finding the system with the best primary performance measure among a finite number of simulated systems in the presence of a stochastic constraint on a single real‐valued secondary performance measure. Solving this problem requires the identification and removal from consideration of infeasible systems (Phase I) and of systems whose primary performance measure is dominated by that of other feasible systems (Phase II). We use indifference zones in both phases and consider two approaches, namely, carrying out Phases I and II sequentially and carrying out Phases I and II simultaneously, and we provide specific example procedures of each type. We present theoretical results guaranteeing that our approaches (general and specific, sequential and simultaneous) yield the best system with at least a prespecified probability, and we provide a portion of an extensive numerical study aimed at evaluating and comparing the performance of our approaches. The experimental results show that both new procedures are useful for constrained ranking and selection, with neither procedure showing uniform superiority over the other.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010

Suggested Citation

  • Sigrún Andradóttir & Seong‐Hee Kim, 2010. "Fully sequential procedures for comparing constrained systems via simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 403-421, August.
    • Handle: RePEc:wly:navres:v:57:y:2010:i:5:p:403-421
    DOI: 10.1002/nav.20408
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    1. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    2. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    3. Pichitlamken, Juta & Nelson, Barry L. & Hong, L. Jeff, 2006. "A sequential procedure for neighborhood selection-of-the-best in optimization via simulation," European Journal of Operational Research, Elsevier, vol. 173(1), pages 283-298, August.
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    Cited by:

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    11. Zhongshun Shi & Siyang Gao & Hui Xiao & Weiwei Chen, 2019. "A worst‐case formulation for constrained ranking and selection with input uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 648-662, December.
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    14. Peter I. Frazier, 2014. "A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection," Operations Research, INFORMS, vol. 62(4), pages 926-942, August.
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