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

• 成果类型: Article
• 署名作者: Luo, Jun; Hong, L. Jeff; Nelson, Barry L.; Wu, Yang
• 署名单位: Shanghai Jiao Tong University; City University of Hong Kong; City University of Hong Kong; Northwestern University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2015.1413
• 发表日期: 2015
• 页码: 1177-1194
• 关键词: simulated system; 2-stage

摘要

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.