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Ranking and Selection: Efficient Simulation Budget Allocation

In: Handbook of Simulation Optimization

Author

Listed:
  • Chun-Hung Chen

    (George Mason University)

  • Stephen E. Chick

    (INSEAD)

  • Loo Hay Lee

    (National University of Singapore)

  • Nugroho A. Pujowidianto

    (Hewlett-Packard Singapore)

Abstract

This chapter reviews the problem of selecting the best of a finite set of alternatives, where best is defined with respect to the highest mean performance, and where the performance is uncertain but may be estimated with simulation. This problem has been explored from several perspectives, including statistical ranking and selection, multiple comparisons, and stochastic optimization. Approaches taken in the literature include frequentist statistics, Bayesian statistics, related heuristics, and asymptotic convergence in probability. This chapter presents algorithms that are derived from Bayesian and related conceptual frameworks to provide empirically effective performance for the ranking and selection problem. In particular, we motivate the optimal computing budget allocation (OCBA) algorithm and expected value of information (EVI) approaches, give example algorithms, and provide pointers to the literature for detailed derivations and extensions of these approaches.

Suggested Citation

  • Chun-Hung Chen & Stephen E. Chick & Loo Hay Lee & Nugroho A. Pujowidianto, 2015. "Ranking and Selection: Efficient Simulation Budget Allocation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 45-80, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-1384-8_3
    DOI: 10.1007/978-1-4939-1384-8_3
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    Citations

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    Cited by:

    1. Eric C. Ni & Dragos F. Ciocan & Shane G. Henderson & Susan R. Hunter, 2017. "Efficient Ranking and Selection in Parallel Computing Environments," Operations Research, INFORMS, vol. 65(3), pages 821-836, June.
    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    3. Ilya O. Ryzhov, 2016. "On the Convergence Rates of Expected Improvement Methods," Operations Research, INFORMS, vol. 64(6), pages 1515-1528, December.
    4. Demet Batur & F. Fred Choobineh, 2021. "Selecting the Best Alternative Based on Its Quantile," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 657-671, May.
    5. David J. Eckman & Shane G. Henderson, 2022. "Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1711-1728, May.
    6. 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.
    7. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    8. Ying Zhong & Shaoxuan Liu & Jun Luo & L. Jeff Hong, 2022. "Speeding Up Paulson’s Procedure for Large-Scale Problems Using Parallel Computing," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 586-606, January.
    9. Ye Chen & Ilya O. Ryzhov, 2023. "Balancing Optimal Large Deviations in Sequential Selection," Management Science, INFORMS, vol. 69(6), pages 3457-3473, June.
    10. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    11. Haihui Shen & L. Jeff Hong & Xiaowei Zhang, 2021. "Ranking and Selection with Covariates for Personalized Decision Making," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1500-1519, October.

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