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Selecting the best stochastic system for large scale problems in DEDS

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  • Alrefaei, Mahmoud H.
  • Alawneh, Ameen J.

Abstract

We consider the problem of selecting the stochastic system with the best expected performance measure, when the number of alternative systems is large. We consider the case of discrete event dynamic systems (DEDS) where the standard clock simulation technique can be used for simulating multiple systems using only one sample path. In this paper, we use a two-phase procedure that uses the standard clock simulation technique. In the first phase, we screen out non-competent alternatives and construct a confidence set that contains the best alternative with a pre-specified large probability. In the second phase, we use the indifference-zone ranking and selection procedure to select the best expected alternative among the survivals of the first phase. We implement this algorithm for solving a practical simulation optimization problem.

Suggested Citation

  • Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2004. "Selecting the best stochastic system for large scale problems in DEDS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 237-245.
    • Handle: RePEc:eee:matcom:v:64:y:2004:i:2:p:237-245
    DOI: 10.1016/j.matcom.2003.09.018
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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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    3. Mahmoud H. Alrefaei & Sigrún Andradóttir, 1999. "A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization," Management Science, INFORMS, vol. 45(5), pages 748-764, May.
    4. Alrefaei, Mahmoud H. & Andradottir, Sigrun, 2001. "A modification of the stochastic ruler method for discrete stochastic optimization," European Journal of Operational Research, Elsevier, vol. 133(1), pages 160-182, August.
    5. David W. Sullivan & James R. Wilson, 1989. "Restricted Subset Selection Procedures for Simulation," Operations Research, INFORMS, vol. 37(1), pages 52-71, February.
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    Cited by:

    1. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.
    2. Teng, Suyan & Lee, Loo Hay & Chew, Ek Peng, 2010. "Integration of indifference-zone with multi-objective computing budget allocation," European Journal of Operational Research, Elsevier, vol. 203(2), pages 419-429, June.
    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. 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.
    5. Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.

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