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Algorithm for Calculating the Initial Sample Size in a Fully Sequential Ranking and Selection Procedure

Author

Listed:
  • Ruijing Wu

    (SISU School of Business and Management, Shanghai International Studies University, Shanghai 200083, P. R. China2Institute of Intelligent Operations and Supply Chain Management, Shanghai International Studies University, Shanghai 200083, P. R. China)

  • Shaoxuan Liu

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, P. R. China)

  • Zhenyang Shi

    (School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China)

Abstract

In some fully sequential ranking and selection procedures, such as the KN procedure and Rinott’s procedure, some initial samples must be taken to estimate the variance. We analyze the impact of the initial sample size (ISS) on the total sample size and propose an algorithm to calculate the ISS in this type of procedure. To better illustrate our approach, we implement this algorithm on the KN procedure and propose the KN-ISS procedure. Comprehensive numerical experiments reveal that this procedure can significantly improve the efficiency compared with the KN procedure and still deliver the desired probability of correct selection.

Suggested Citation

  • Ruijing Wu & Shaoxuan Liu & Zhenyang Shi, 2020. "Algorithm for Calculating the Initial Sample Size in a Fully Sequential Ranking and Selection Procedure," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(03), pages 1-19, May.
    • Handle: RePEc:wsi:apjorx:v:37:y:2020:i:03:n:s0217595920500153
    DOI: 10.1142/S0217595920500153
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    Cited by:

    1. Cheng, Zhenxia & Luo, Jun & Wu, Ruijing, 2023. "On the finite-sample statistical validity of adaptive fully sequential procedures," European Journal of Operational Research, Elsevier, vol. 307(1), pages 266-278.

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