IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v49y2001i6p950-963.html
   My bibliography  Save this article

Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large

Author

Listed:
  • Barry L. Nelson

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208-3119)

  • Julie Swann

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • David Goldsman

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Wheyming Song

    (Department of Industrial Engineering, National Tsing Hua University, Hsinchu R.O.C., Taiwan)

Abstract

In this paper, we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is finite, but large enough that ranking-and-selection (R&S) procedures may require too much computation to be practical. Our approach is to use the data provided by the first stage of sampling in an R&S procedure to screen out alternatives that are not competitive, and thereby avoid the (typically much larger) second-stage sample for these systems. Our procedures represent a compromise between standard R&S procedures---which are easy to implement, but can be computationally inefficient---and fully sequential procedures---which can be statistically efficient, but are more difficult to implement and depend on more restrictive assumptions. We present a general theory for constructing combined screening and indifference-zone selection procedures, several specific procedures and a portion of an extensive empirical evaluation.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:49:y:2001:i:6:p:950-963
    DOI: 10.1287/opre.49.6.950.10019
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.49.6.950.10019
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.49.6.950.10019?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. David W. Sullivan & James R. Wilson, 1989. "Corrections to “Restricted Subset Selection Procedures for Simulation”," Operations Research, INFORMS, vol. 37(4), pages 673-673, August.
    2. Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
    3. Barry L. Nelson & Frank J. Matejcik, 1995. "Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation," Management Science, INFORMS, vol. 41(12), pages 1935-1945, December.
    4. David W. Sullivan & James R. Wilson, 1989. "Restricted Subset Selection Procedures for Simulation," Operations Research, INFORMS, vol. 37(1), pages 52-71, February.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Mohammad H. Almomani & Mahmoud H. Alrefaei, 2016. "Ordinal Optimization with Computing Budget Allocation for Selecting an Optimal Subset," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-17, April.
    3. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.
    4. 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.
    5. Vasilii Erokhin & Li Diao & Tianming Gao & Jean-Vasile Andrei & Anna Ivolga & Yuhang Zong, 2021. "The Supply of Calories, Proteins, and Fats in Low-Income Countries: A Four-Decade Retrospective Study," IJERPH, MDPI, vol. 18(14), pages 1-30, July.
    6. Tianming Gao & Vasilii Erokhin, 2020. "Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North," Sustainability, MDPI, vol. 12(5), pages 1-25, March.
    7. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    8. Chun-Hung Chen & Donghai He & Michael Fu & Loo Hay Lee, 2008. "Efficient Simulation Budget Allocation for Selecting an Optimal Subset," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 579-595, November.
    9. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    10. Batur, D. & Choobineh, F., 2010. "A quantile-based approach to system selection," European Journal of Operational Research, Elsevier, vol. 202(3), pages 764-772, May.
    11. Michael C. Fu & Jian-Qiang Hu & Chun-Hung Chen & Xiaoping Xiong, 2007. "Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 101-111, February.
    12. Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.
    13. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    14. Stephen E. Chick & Noah Gans, 2009. "Economic Analysis of Simulation Selection Problems," Management Science, INFORMS, vol. 55(3), pages 421-437, March.
    15. 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.
    16. Stephen E. Chick & Koichiro Inoue, 2001. "New Procedures to Select the Best Simulated System Using Common Random Numbers," Management Science, INFORMS, vol. 47(8), pages 1133-1149, August.
    17. Anton J. Kleywegt & Vijay S. Nori & Martin W. P. Savelsbergh, 2002. "The Stochastic Inventory Routing Problem with Direct Deliveries," Transportation Science, INFORMS, vol. 36(1), pages 94-118, February.
    18. Kleijnen, J.P.C. & Sanchez, S.M. & Lucas, T.W. & Cioppa, T.M., 2003. "A User's Guide to the Brave New World of Designing Simulation Experiments," Discussion Paper 2003-1, Tilburg University, Center for Economic Research.
    19. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    20. Diana M. Negoescu & Peter I. Frazier & Warren B. Powell, 2011. "The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 346-363, August.

    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:49:y:2001:i:6:p:950-963. 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.