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A direct search method for unconstrained quantile-based simulation optimization

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  • Chang, Kuo-Hao

Abstract

Simulation optimization has gained popularity over the decades because of its ability to solve many practical problems that involve profound randomness. The methodology development of simulation optimization, however, is largely concerned with problems whose objective function is mean-based performance metric. In this paper, we propose a direct search method to solve the unconstrained simulation optimization problems with quantile-based objective functions. Because the proposed method does not require gradient estimation in the search process, it can be applied to solve many practical problems where the gradient of objective function does not exist or is difficult to estimate. We prove that the proposed method possesses desirable convergence guarantee, i.e., the algorithm can converge to the true global optima with probability one. An extensive numerical study shows that the performance of the proposed method is promising. Two illustrative examples are provided in the end to demonstrate the viability of the proposed method in real settings.

Suggested Citation

  • Chang, Kuo-Hao, 2015. "A direct search method for unconstrained quantile-based simulation optimization," European Journal of Operational Research, Elsevier, vol. 246(2), pages 487-495.
  • Handle: RePEc:eee:ejores:v:246:y:2015:i:2:p:487-495
    DOI: 10.1016/j.ejor.2015.05.010
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    References listed on IDEAS

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    1. Michael C. Fu & L. Jeff Hong & Jian-Qiang Hu, 2009. "Conditional Monte Carlo Estimation of Quantile Sensitivities," Management Science, INFORMS, vol. 55(12), pages 2019-2027, December.
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    4. Kuo-Hao Chang & L. Jeff Hong & Hong Wan, 2013. "Stochastic Trust-Region Response-Surface Method (STRONG)---A New Response-Surface Framework for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 230-243, May.
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    Cited by:

    1. Chang, Kuo-Hao & Cuckler, Robert & Lee, Song-Lin & Lee, Loo Hay, 2022. "Discrete conditional-expectation-based simulation optimization: Methodology and applications," European Journal of Operational Research, Elsevier, vol. 298(1), pages 213-228.
    2. Chang, Kuo-Hao & Kuo, Po-Yi, 2018. "An efficient simulation optimization method for the generalized redundancy allocation problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1094-1101.

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