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Comparisons with a Standard in Simulation Experiments

Author

Listed:
  • Barry L. Nelson

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

  • David Goldsman

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

Abstract

We consider the problem of comparing a finite number of stochastic systems with respect to a single system (designated as the "standard") via simulation experiments. The comparison is based on expected performance, and the goal is to determine if any system has larger expected performance than the standard, and if so to identify the best of the alternatives. In this paper we provide two-stage experiment design and analysis procedures to solve the problem for a variety of scenarios, including those in which we encounter unequal variances across systems, as well as those in which we use the variance reduction technique of common random numbers and it is appropriate to do so. The emphasis is added because in some cases common random numbers can be counterproductive when performing comparisons with a standard. We also provide methods for estimating the critical constants required by our procedures, present a portion of an extensive empirical study, and demonstrate one of the procedures via a numerical example.

Suggested Citation

  • Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
    • Handle: RePEc:inm:ormnsc:v:47:y:2001:i:3:p:449-463
    DOI: 10.1287/mnsc.47.3.449.9778
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    References listed on IDEAS

    as
    1. Frank J. Matejcik & Barry L. Nelson, 1995. "Two-Stage Multiple Comparisons with the Best for Computer Simulation," Operations Research, INFORMS, vol. 43(4), pages 633-640, August.
    2. Wei-Ning Yang & Barry L. Nelson, 1991. "Using Common Random Numbers and Control Variates in Multiple-Comparison Procedures," Operations Research, INFORMS, vol. 39(4), pages 583-591, August.
    3. Paul Desruelle & Harold J. Steudel, 1996. "A Queuing Network Model of a Single-Operator Manufacturing Workcell with Machine/Operator Interference," Management Science, INFORMS, vol. 42(4), pages 576-590, April.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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," Other publications TiSEM a6910d11-f9bc-4246-b1a7-2, Tilburg University, School of Economics and Management.
    2. Tsai, Shing Chih & Chu, I-Hao, 2012. "Controlled multistage selection procedures for comparison with a standard," European Journal of Operational Research, Elsevier, vol. 223(3), pages 709-721.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.
    9. 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.
    10. 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.
    11. 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.
    12. L. Jeff Hong & Jun Luo & Barry L. Nelson, 2015. "Chance Constrained Selection of the Best," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 317-334, May.
    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. 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.
    16. Hong Wan & Bruce E. Ankenman & Barry L. Nelson, 2006. "Controlled Sequential Bifurcation: A New Factor-Screening Method for Discrete-Event Simulation," Operations Research, INFORMS, vol. 54(4), pages 743-755, August.
    17. Shing Chih Tsai & Tse Yang, 2017. "Rapid screening algorithms for stochastically constrained problems," Annals of Operations Research, Springer, vol. 254(1), pages 425-447, July.

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