IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v62y2014i4p926-942.html
   My bibliography  Save this article

A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection

Author

Listed:
  • Peter I. Frazier

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

Abstract

We consider the indifference-zone (IZ) formulation of the ranking and selection problem with independent normal samples. In this problem, we must use stochastic simulation to select the best among several noisy simulated systems, with a statistical guarantee on solution quality. Existing IZ procedures sample excessively in problems with many alternatives, in part because loose bounds on probability of correct selection lead them to deliver solution quality much higher than requested. Consequently, existing IZ procedures are seldom considered practical for problems with more than a few hundred alternatives. To overcome this, we present a new sequential elimination IZ procedure, called BIZ (Bayes-inspired indifference zone), whose lower bound on worst-case probability of correct selection in the preference zone is tight in continuous time, and nearly tight in discrete time. To the author's knowledge, this is the first sequential elimination procedure with tight bounds on worst-case preference-zone probability of correct selection for more than two alternatives. Theoretical results for the discrete-time case assume that variances are known and have an integer multiple structure, but the BIZ procedure itself can be used when these assumptions are not met. In numerical experiments, the sampling effort used by BIZ is significantly smaller than that of another leading IZ procedure, the KN procedure, especially on the largest problems tested (2 14 = 16,384 alternatives).

Suggested Citation

  • Peter I. Frazier, 2014. "A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection," Operations Research, INFORMS, vol. 62(4), pages 926-942, August.
  • Handle: RePEc:inm:oropre:v:62:y:2014:i:4:p:926-942
    DOI: 10.1287/opre.2014.1282
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2014.1282
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.2014.1282?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    2. 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.
    3. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Other publications TiSEM e49ba0fc-853c-4a13-b564-d, Tilburg University, School of Economics and Management.
    4. Sigrún Andradóttir & Seong‐Hee Kim, 2010. "Fully sequential procedures for comparing constrained systems via simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 403-421, August.
    5. L. Jeff Hong, 2006. "Fully sequential indifference‐zone selection procedures with variance‐dependent sampling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 464-476, August.
    6. David Goldsman & Seong-Hee Kim & William S. Marshall & Barry L. Nelson, 2002. "Ranking and Selection for Steady-State Simulation: Procedures and Perspectives," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 2-19, February.
    7. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
    8. Stephen E. Chick & Jürgen Branke & Christian Schmidt, 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 71-80, February.
    9. Stephen E. Chick & Koichiro Inoue, 2001. "New Two-Stage and Sequential Procedures for Selecting the Best Simulated System," Operations Research, INFORMS, vol. 49(5), pages 732-743, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    2. Jiejian Feng & Michael Zhang, 2017. "Reducing cost and abandoned E-components in incomplete identification," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(3), pages 281-290, March.
    3. Lee, Mi Lim & Park, Chuljin & Park, Dong Uk, 2018. "Self-adjusting the tolerance level in a fully sequential feasibility check procedure," European Journal of Operational Research, Elsevier, vol. 271(2), pages 733-745.
    4. Weiwei Fan & L. Jeff Hong & Barry L. Nelson, 2016. "Indifference-Zone-Free Selection of the Best," Operations Research, INFORMS, vol. 64(6), pages 1499-1514, December.
    5. Michael Ludkovski & James Risk, 2017. "Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement," Papers 1710.05204, arXiv.org, revised May 2018.
    6. 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.
    7. Weiwei Fan & L. Jeff Hong & Xiaowei Zhang, 2020. "Distributionally Robust Selection of the Best," Management Science, INFORMS, vol. 66(1), pages 190-208, January.
    8. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    9. Demet Batur & F. Fred Choobineh, 2021. "Selecting the Best Alternative Based on Its Quantile," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 657-671, May.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhongshun Shi & Siyang Gao & Hui Xiao & Weiwei Chen, 2019. "A worst‐case formulation for constrained ranking and selection with input uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 648-662, December.
    2. Shing Chih Tsai, 2013. "Rapid Screening Procedures for Zero-One Optimization via Simulation," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 317-331, May.
    3. Gongbo Zhang & Yijie Peng & Jianghua Zhang & Enlu Zhou, 2023. "Asymptotically Optimal Sampling Policy for Selecting Top- m Alternatives," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1261-1285, November.
    4. Kabirian, Alireza & Ólafsson, Sigurdur, 2011. "Continuous optimization via simulation using Golden Region search," European Journal of Operational Research, Elsevier, vol. 208(1), pages 19-27, January.
    5. Juergen Branke & Wen Zhang, 2019. "Identifying efficient solutions via simulation: myopic multi-objective budget allocation for the bi-objective case," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 831-865, September.
    6. Kleijnen, Jack P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2011. "Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62," Discussion Paper 2011-015, Tilburg University, Center for Economic Research.
    7. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.
    8. Kleijnen, Jack P.C., 2013. "Simulation-Optimization via Kriging and Bootstrapping : A Survey (Revision of CentER DP 2011-064)," Other publications TiSEM 6ac4e049-ad86-447f-aeec-a, Tilburg University, School of Economics and Management.
    9. Jing Xie & Peter I. Frazier, 2013. "Sequential Bayes-Optimal Policies for Multiple Comparisons with a Known Standard," Operations Research, INFORMS, vol. 61(5), pages 1174-1189, October.
    10. Tsai, Shing Chih & Fu, Sheng Yang, 2014. "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, Elsevier, vol. 236(1), pages 113-125.
    11. Demet Batur & Lina Wang & F. Fred Choobineh, 2018. "Methods for System Selection Based on Sequential Mean–Variance Analysis," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 724-738, November.
    12. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    13. 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.
    14. Weiwei Chen & Siyang Gao & Wenjie Chen & Jianzhong Du, 2023. "Optimizing resource allocation in service systems via simulation: A Bayesian formulation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 65-81, January.
    15. 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.
    16. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    17. Weiwei Fan & L. Jeff Hong & Barry L. Nelson, 2016. "Indifference-Zone-Free Selection of the Best," Operations Research, INFORMS, vol. 64(6), pages 1499-1514, December.
    18. Lee, Loo Hay & Chew, Ek Peng & Manikam, Puvaneswari, 2006. "A general framework on the simulation-based optimization under fixed computing budget," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1828-1841, November.
    19. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    20. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:62:y:2014:i:4:p:926-942. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.