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Controlled multistage selection procedures for comparison with a standard

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  • Tsai, Shing Chih
  • Chu, I-Hao
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    Abstract

    Comparison with a standard is a general multiple comparison problem, where each system is required to be compared to a single system, referred to as a “standard”, as well as to other alternative systems. The goal is to determine the best system among a number of systems that are better than the standard, or to select the standard when it is equal to or better than the other alternatives. Kim (2005) proposed an efficient fully sequential procedure for comparison with a standard, that obtains a single observation at each stage from the surviving systems, and is one of the most efficient existing procedures. We develop two provably valid multistage selection procedures that take a number of observations from each system and update the variance estimators at each stage. We also employ appropriate control variate technique for each procedure to further improve the efficiency. Empirical results are provided to demonstrate that the proposed procedures are statistically and computationally more efficient than existing fully sequential procedures.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 223 (2012)
    Issue (Month): 3 ()
    Pages: 709-721

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    Handle: RePEc:eee:ejores:v:223:y:2012:i:3:p:709-721

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Simulation; Comparison with a standard; Variance reduction techniques; Output analysis;

    References

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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.
    2. Pichitlamken, Juta & Nelson, Barry L. & Hong, L. Jeff, 2006. "A sequential procedure for neighborhood selection-of-the-best in optimization via simulation," European Journal of Operational Research, Elsevier, vol. 173(1), pages 283-298, August.
    3. S. S. Lavenberg & P. D. Welch, 1981. "A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations," Management Science, INFORMS, vol. 27(3), pages 322-335, March.
    4. Sriver, Todd A. & Chrissis, James W. & Abramson, Mark A., 2009. "Pattern search ranking and selection algorithms for mixed variable simulation-based optimization," European Journal of Operational Research, Elsevier, vol. 198(3), pages 878-890, November.
    5. Vadim Lesnevski & Barry L. Nelson & Jeremy Staum, 2007. "Simulation of Coherent Risk Measures Based on Generalized Scenarios," Management Science, INFORMS, vol. 53(11), pages 1756-1769, November.
    6. 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.
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    Cited by:
    1. Tsai, Shing Chih & Zheng, Ya-Xin, 2013. "A simulation optimization approach for a two-echelon inventory system with service level constraints," European Journal of Operational Research, Elsevier, vol. 229(2), pages 364-374.
    2. Tsai, Shing Chih & Fu, Sheng Yang, 2014. "Genetic-algorithm-based simulation optimization considering a single stochastic constraint," European Journal of Operational Research, Elsevier, vol. 236(1), pages 113-125.

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