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Selecting a Selection Procedure

Author

Listed:
  • Jürgen Branke

    (Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany)

  • Stephen E. Chick

    (Technology and Operations Management, INSEAD, 77305 Fontainebleau Cedex, France)

  • Christian Schmidt

    (Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe, Germany)

Abstract

Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. "Best" is defined with respect to the largest mean, but the mean is inferred with statistical sampling, as in simulation optimization. There are a wide variety of procedures, which begs the question of which selection procedure to select. The main contribution of this paper is to identify, through extensive experimentation, the most effective selection procedures when samples are independent and normally distributed. We also (a) summarize the main structural approaches to deriving selection procedures, (b) formalize new sampling allocations and stopping rules, (c) identify strengths and weaknesses of the procedures, (d) identify some theoretical links between them, and (e) present an innovative empirical test bed with the most extensive numerical comparison of selection procedures to date. The most efficient and easiest to control procedures allocate samples with a Bayesian model for uncertainty about the means and use new adaptive stopping rules proposed here.

Suggested Citation

  • Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.
    • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:12:p:1916-1932
    DOI: 10.1287/mnsc.1070.0721
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    References listed on IDEAS

    as
    1. Chen, E. Jack & Kelton, W. David, 2005. "Sequential selection procedures: Using sample means to improve efficiency," European Journal of Operational Research, Elsevier, vol. 166(1), pages 133-153, October.
    2. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    3. Michael C. Fu & Jian-Qiang Hu & Chun-Hung Chen & Xiaoping Xiong, 2007. "Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 101-111, February.
    4. 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.
    5. Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
    6. Stephen E. Chick & Koichiro Inoue, 2002. "Corrigendum: New Selection Procedures," Operations Research, INFORMS, vol. 50(3), pages 566-566, June.
    7. Stephen E. Chick & Yaozhong Wu, 2005. "Selection Procedures with Frequentist Expected Opportunity Cost Bounds," Operations Research, INFORMS, vol. 53(5), pages 867-878, October.
    8. 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.
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    Cited by:

    1. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
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    3. Groves, Matthew & Branke, Juergen, 2019. "Top-κ selection with pairwise comparisons," European Journal of Operational Research, Elsevier, vol. 274(2), pages 615-626.
    4. Tsai, Shing Chih & Fu, Sheng Yang, 2014. "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, Elsevier, vol. 236(1), pages 113-125.
    5. Wang, Tianxiang & Xu, Jie & Hu, Jian-Qiang & Chen, Chun-Hung, 2023. "Efficient estimation of a risk measure requiring two-stage simulation optimization," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1355-1365.
    6. Fei, Xin & Gülpınar, Nalân & Branke, Jürgen, 2019. "Efficient solution selection for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 277(3), pages 918-929.
    7. Tan, Kim Hua & Noble, James & Sato, Yuji & Tse, Ying Kei, 2011. "A marginal analysis guided technology evaluation and selection," International Journal of Production Economics, Elsevier, vol. 131(1), pages 15-21, May.
    8. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.

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