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Discrete stochastic optimization using variants of the stochastic ruler method

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  • Mahmoud H. Alrefaei
  • Sigrún Andradóttir

Abstract

We present two random search methods for solving discrete stochastic optimization problems. Both of these methods are variants of the stochastic ruler algorithm. They differ from our earlier modification of the stochastic ruler algorithm in that they use different approaches for estimating the optimal solution. Our new methods are guaranteed to converge almost surely to the set of global optimal solutions under mild conditions. We discuss under what conditions these new methods are expected to converge faster than the modified stochastic ruler algorithm. We also discuss how these methods can be used for solving discrete optimization problems when the values of the objective function are estimated using either transient or steady‐state simulation. Finally, we present numerical results that compare the performance of our new methods with that of the modified stochastic ruler algorithm when applied to solve buffer allocation problems. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Suggested Citation

  • Mahmoud H. Alrefaei & Sigrún Andradóttir, 2005. "Discrete stochastic optimization using variants of the stochastic ruler method," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(4), pages 344-360, June.
    • Handle: RePEc:wly:navres:v:52:y:2005:i:4:p:344-360
    DOI: 10.1002/nav.20080
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Vladimir I. Norkin & Yuri M. Ermoliev & Andrzej Ruszczyński, 1998. "On Optimal Allocation of Indivisibles Under Uncertainty," Operations Research, INFORMS, vol. 46(3), pages 381-395, June.
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    Cited by:

    1. Tahir Ekin & Stephen Walker & Paul Damien, 2023. "Augmented simulation methods for discrete stochastic optimization with recourse," Annals of Operations Research, Springer, vol. 320(2), pages 771-793, January.
    2. Andrei A. Prudius & Sigrún Andradóttir, 2012. "Averaging frameworks for simulation optimization with applications to simulated annealing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 411-429, September.
    3. Qiushi Chen & Lei Zhao & Jan C. Fransoo & Zhe Li, 2019. "Dual-mode inventory management under a chance credit constraint," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 147-178, March.

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