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

Ruijing Wu, Shaoxuan Liu and Zhenyang Shi ()
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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

Asia-Pacific Journal of Operational Research (APJOR), 2020, vol. 37, issue 03, 1-19

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.

Keywords: Simulation; initial sample size; fully sequential procedures; ranking and selection (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0217595920500153

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