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Restricted Subset Selection Procedures for Simulation

Author

Listed:
  • David W. Sullivan

    (Lockheed Missiles & Space Co., and The University of Texas, Austin, Texas)

  • James R. Wilson

    (Purdue University, West Lafayette, Indiana)

Abstract

This paper develops two procedures for screening a set of normal populations with unknown moments in order that the final subset of selected populations satisfies the following requirements involving the user specified parameters P *, δ, and m : With probability at least P *, the selected subset will contain a population whose mean lies less than the distance δ from the largest mean. Although the size of the selected subset is random, at most m populations will finally be chosen, where m is usually taken small enough to reserve adequate resources for a more intensive follow-up study. The exact procedure V E is a two-stage random sampling scheme that is designed to compare transient or steady-state simulation models based on independent replications of each model. The heuristic procedure V S is a modification of V E that is designed to compare steady-state simulation models based on a single prolonged run of each model. A complete justification is given for V E together with tables of the constants required by both procedures and an algorithm for computing these constants. Experimental results are summarized to gauge the robustness of V E against departures from normality and to evaluate the performance of V S on several types of stationary stochastic processes.

Suggested Citation

  • David W. Sullivan & James R. Wilson, 1989. "Restricted Subset Selection Procedures for Simulation," Operations Research, INFORMS, vol. 37(1), pages 52-71, February.
    • Handle: RePEc:inm:oropre:v:37:y:1989:i:1:p:52-71
    DOI: 10.1287/opre.37.1.52
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    Citations

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    Cited by:

    1. David Goldsman & Seong-Hee Kim & William S. Marshall & Barry L. Nelson, 2002. "Ranking and Selection for Steady-State Simulation: Procedures and Perspectives," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 2-19, February.
    2. Tianming Gao & Vasilii Erokhin, 2020. "Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North," Sustainability, MDPI, vol. 12(5), pages 1-25, March.
    3. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.
    4. Weiwei Fan & L. Jeff Hong & Barry L. Nelson, 2016. "Indifference-Zone-Free Selection of the Best," Operations Research, INFORMS, vol. 64(6), pages 1499-1514, December.
    5. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2004. "Selecting the best stochastic system for large scale problems in DEDS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 237-245.
    6. Vasilii Erokhin & Li Diao & Tianming Gao & Jean-Vasile Andrei & Anna Ivolga & Yuhang Zong, 2021. "The Supply of Calories, Proteins, and Fats in Low-Income Countries: A Four-Decade Retrospective Study," IJERPH, MDPI, vol. 18(14), pages 1-30, July.
    7. Chun-Hung Chen & Donghai He & Michael Fu & Loo Hay Lee, 2008. "Efficient Simulation Budget Allocation for Selecting an Optimal Subset," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 579-595, November.
    8. Mohammad H. Almomani & Mahmoud H. Alrefaei, 2016. "Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-17, April.
    9. 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.

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