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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jun Luo & L. Jeff Hong & Barry L. Nelson & Yang Wu, 2015. "Fully Sequential Procedures for Large-Scale Ranking-and-Selection Problems in Parallel Computing Environments," Operations Research, INFORMS, vol. 63(5), pages 1177-1194, October.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:5:p:1177-1194
    DOI: 10.1287/opre.2015.1413
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    References listed on IDEAS

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    1. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
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    6. Pichitlamken, Juta & Nelson, Barry L. & Hong, L. Jeff, 2006. "A sequential procedure for neighborhood selection-of-the-best in optimization via simulation," European Journal of Operational Research, Elsevier, vol. 173(1), pages 283-298, August.
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    Citations

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    Cited by:

    1. Ying Zhong & Shaoxuan Liu & Jun Luo & L. Jeff Hong, 2022. "Speeding Up Paulson’s Procedure for Large-Scale Problems Using Parallel Computing," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 586-606, January.
    2. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    3. Eric C. Ni & Dragos F. Ciocan & Shane G. Henderson & Susan R. Hunter, 2017. "Efficient Ranking and Selection in Parallel Computing Environments," Operations Research, INFORMS, vol. 65(3), pages 821-836, June.
    4. Peng, Rui & He, Xiaofeng & Zhong, Chao & Kou, Gang & Xiao, Hui, 2022. "Preventive maintenance for heterogeneous parallel systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. 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.
    6. Taylor, Simon J.E., 2019. "Distributed simulation: state-of-the-art and potential for operational research," European Journal of Operational Research, Elsevier, vol. 273(1), pages 1-19.
    7. 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.
    8. Saeid Delshad & Amin Khademi, 2020. "Information theory for ranking and selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 239-253, June.
    9. Zhongshun Shi & Yijie Peng & Leyuan Shi & Chun-Hung Chen & Michael C. Fu, 2022. "Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 557-568, January.
    10. David J. Eckman & Shane G. Henderson, 2022. "Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1711-1728, May.
    11. Sankaranarayanan, Sriram & Feijoo, Felipe & Siddiqui, Sauleh, 2018. "Sensitivity and covariance in stochastic complementarity problems with an application to North American natural gas markets," European Journal of Operational Research, Elsevier, vol. 268(1), pages 25-36.
    12. Shing Chih Tsai & Jun Luo & Chi Ching Sung, 2017. "Combined variance reduction techniques in fully sequential selection procedures," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(6), pages 502-527, September.
    13. Haihui Shen & L. Jeff Hong & Xiaowei Zhang, 2021. "Ranking and Selection with Covariates for Personalized Decision Making," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1500-1519, October.
    14. Ruijing Wu & Shaoxuan Liu & Zhenyang Shi, 2019. "Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information," Sustainability, MDPI, vol. 11(11), pages 1-24, May.
    15. L. Jeff Hong & Guangxin Jiang, 2019. "Offline Simulation Online Application: A New Framework of Simulation-Based Decision Making," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-22, December.
    16. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.
    17. Wang, Bo & Zhang, Qiong & Xie, Wei, 2019. "Bayesian sequential data collection for stochastic simulation calibration," European Journal of Operational Research, Elsevier, vol. 277(1), pages 300-316.

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