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A quantile-based approach to system selection

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  • Batur, D.
  • Choobineh, F.

Abstract

We propose a quantile-based ranking and selection (R&S) procedure for comparing a finite set of stochastic systems via simulation. Our R&S procedure uses a quantile set of the simulated probability distribution of a performance characteristic of interest that best represents the most appropriate selection criterion as the basis for comparison. Since this quantile set may represent either the downside risk, upside risk, or central tendency of the performance characteristic, the proposed approach is more flexible than the traditional mean-based approach to R&S. We first present a procedure that selects the best system from among K systems, and then we modified that procedure for the case where KÂ -Â 1 systems are compared against a standard system. We present a set of experiments to highlight the flexibility of the proposed procedures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:3:p:764-772
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    References listed on IDEAS

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    1. Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
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    6. Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2022. "Evaluation of VaR and CVaR for the makespan in interval valued blocking job shops," International Journal of Production Economics, Elsevier, vol. 247(C).
    5. Demet Batur & F. Fred Choobineh, 2021. "Selecting the Best Alternative Based on Its Quantile," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 657-671, May.
    6. Kleijnen, Jack P.C. & Pierreval, Henri & Zhang, Jin, 2011. "Methodology for determining the acceptability of system designs in uncertain environments," European Journal of Operational Research, Elsevier, vol. 209(2), pages 176-183, March.
    7. Yijie Peng & Chun-Hung Chen & Michael C. Fu & Jian-Qiang Hu & Ilya O. Ryzhov, 2021. "Efficient Sampling Allocation Procedures for Optimal Quantile Selection," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 230-245, January.
    8. Gabriella Dellino & Jack P. C. Kleijnen & Carlo Meloni, 2012. "Robust Optimization in Simulation: Taguchi and Krige Combined," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 471-484, August.
    9. J P C Kleijnen & W C M van Beers, 2013. "Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(5), pages 708-717, May.
    10. Cheng, Zhenxia & Luo, Jun & Wu, Ruijing, 2023. "On the finite-sample statistical validity of adaptive fully sequential procedures," European Journal of Operational Research, Elsevier, vol. 307(1), pages 266-278.
    11. Batur, D. & Choobineh, F., 2012. "Stochastic dominance based comparison for system selection," European Journal of Operational Research, Elsevier, vol. 220(3), pages 661-672.
    12. Dongwook Shin & Mark Broadie & Assaf Zeevi, 2022. "Practical Nonparametric Sampling Strategies for Quantile-Based Ordinal Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 752-768, March.
    13. Saurabh Bansal & Genaro J. Gutierrez, 2020. "Estimating Uncertainties Using Judgmental Forecasts with Expert Heterogeneity," Operations Research, INFORMS, vol. 68(2), pages 363-380, March.

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