Fully Sequential Procedures for Large-Scale Ranking-and-Selection Problems in Parallel Computing Environments

Jun Luo (), L. Jeff Hong (), Barry L. Nelson () and Yang Wu ()
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Jun Luo: Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China 200052
L. Jeff Hong: Department of Economics and Finance and Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Yang Wu: Tmall Company, the Alibaba Group, Hangzhou, Zhejiang, China 310000

Operations Research, 2015, vol. 63, issue 5, 1177-1194

Abstract: Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of simulated alternatives are often designed to be implemented on a single processor. However, parallel computing environments, such as multi-core personal computers and many-core servers, are becoming ubiquitous and easily accessible for ordinary users. In this paper, we propose two types of fully sequential procedures that can be used in parallel computing environments. We call them vector-filling procedures and asymptotic parallel selection procedures, respectively. Extensive numerical experiments show that the proposed procedures can take advantage of multiple parallel processors and solve large-scale R&S problems.

Keywords: fully sequential procedures; parallel computing; statistical issues; asymptotic validity (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (18)

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