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Ranking and selection for terminating simulation under sequential sampling

Author

Listed:
  • Hui Xiao
  • Loo Hay Lee
  • Douglas Morrice
  • Chun-Hung Chen
  • Xiang Hu

Abstract

This research develops an efficient ranking and selection procedure to select the best design for terminating simulation under sequential sampling. This approach enables us to obtain an accurate estimate of the mean performance at a particular point using regression in the case of a terminating simulation. The sequential sampling constraint is imposed to fully utilize the information along the simulation replication. The asymptotically optimal simulation budget allocation among all designs is derived concurrently with the optimal simulation run length and optimal number of simulation groups for each design. To implement the simulation budget allocation rule with a fixed finite simulation budget, a heuristic sequential simulation procedure is suggested with the objective of maximizing the probability of correct selection. Numerical experiments confirm the efficiency of the procedure relative to extant approaches.

Suggested Citation

  • Hui Xiao & Loo Hay Lee & Douglas Morrice & Chun-Hung Chen & Xiang Hu, 2021. "Ranking and selection for terminating simulation under sequential sampling," IISE Transactions, Taylor & Francis Journals, vol. 53(7), pages 735-750, April.
    • Handle: RePEc:taf:uiiexx:v:53:y:2021:i:7:p:735-750
    DOI: 10.1080/24725854.2020.1785647
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    Cited by:

    1. L. Jeff Hong & Guangxin Jiang & Ying Zhong, 2022. "Solving Large-Scale Fixed-Budget Ranking and Selection Problems," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2930-2949, November.
    2. 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.

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