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Optimization of static simulation models by the score function method

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  • Rubinstein, Reuven Y.
  • Shapiro, Alexander

Abstract

In this paper we show how an optimization problem involving the expected performance of a stochastic system can be estimated using a single simulation experiment. The proposed method is based on a probability measure transformation and generation of a stochastic counterpart to the deterministic optimization program. Statistical properties of the derived estimators are discussed and examples are given.

Suggested Citation

  • Rubinstein, Reuven Y. & Shapiro, Alexander, 1990. "Optimization of static simulation models by the score function method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(4), pages 373-392.
    • Handle: RePEc:eee:matcom:v:32:y:1990:i:4:p:373-392
    DOI: 10.1016/0378-4754(90)90142-6
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    References listed on IDEAS

    as
    1. Rajan Suri & Michael A. Zazanis, 1988. "Perturbation Analysis Gives Strongly Consistent Sensitivity Estimates for the M/G/1 Queue," Management Science, INFORMS, vol. 34(1), pages 39-64, January.
    2. Rubinstein, Reuven Y., 1986. "The score function approach for sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(5), pages 351-379.
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    Cited by:

    1. G. Guerkan & A.Y. Oezge & S.M. Robinson, 1994. "Sample-Path Optimization in Simulation," Working Papers wp94070, International Institute for Applied Systems Analysis.
    2. Leyuan Shi & Sigurdur O´lafsson, 2000. "Nested Partitions Method for Stochastic Optimization," Methodology and Computing in Applied Probability, Springer, vol. 2(3), pages 271-291, September.
    3. Nanjing Jian & Shane G. Henderson, 2020. "Estimating the Probability that a Function Observed with Noise Is Convex," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 376-389, April.
    4. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    5. Nathaniel D. Bastian & Pat McMurry & Lawrence V. Fulton & Paul M. Griffin & Shisheng Cui & Thor Hanson & Sharan Srinivas, 2015. "The AMEDD Uses Goal Programming to Optimize Workforce Planning Decisions," Interfaces, INFORMS, vol. 45(4), pages 305-324, August.
    6. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    7. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.

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