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Integration of indifference-zone with multi-objective computing budget allocation

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  • Teng, Suyan
  • Lee, Loo Hay
  • Chew, Ek Peng

Abstract

In this paper, we consider how to address the issues of having designs with close performance in the multi-objective ranking and selection (MORS) problem. To resolve this issue we propose integrating the indifference-zone (IZ) concept into the multi-objective computing budget allocation (MOCBA) framework. In particular, when IZ is introduced into the MOCBA framework, we address how to determine the probability of non-dominance, how to define the Pareto set, and how to derive allocation rules for the simulation replications. Empirical results show that the MOCBA framework with IZ can significantly save simulation budget when designs to be compared have close performance.

Suggested Citation

  • Teng, Suyan & Lee, Loo Hay & Chew, Ek Peng, 2010. "Integration of indifference-zone with multi-objective computing budget allocation," European Journal of Operational Research, Elsevier, vol. 203(2), pages 419-429, June.
  • Handle: RePEc:eee:ejores:v:203:y:2010:i:2:p:419-429
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    References listed on IDEAS

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    1. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    2. Chen, E. Jack & Kelton, W. David, 2005. "Sequential selection procedures: Using sample means to improve efficiency," European Journal of Operational Research, Elsevier, vol. 166(1), pages 133-153, October.
    3. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    4. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2004. "Selecting the best stochastic system for large scale problems in DEDS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 237-245.
    5. Thabane, Lehana & Drekic, Steve, 2003. "Hypothesis testing for the generalized multivariate modified Bessel model," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 360-374, August.
    6. Gupta, Arjun K. & Harrar, Solomon W. & Fujikoshi, Yasunori, 2006. "Asymptotics for testing hypothesis in some multivariate variance components model under non-normality," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 148-178, January.
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    Cited by:

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    2. Healey, Christopher M. & Andradóttir, Sigrún & Kim, Seong-Hee, 2013. "Efficient comparison of constrained systems using dormancy," European Journal of Operational Research, Elsevier, vol. 224(2), pages 340-352.
    3. Wang, Tianxiang & Xu, Jie & Hu, Jian-Qiang & Chen, Chun-Hung, 2023. "Efficient estimation of a risk measure requiring two-stage simulation optimization," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1355-1365.
    4. Mattila, V. & Virtanen, K., 2015. "Ranking and selection for multiple performance measures using incomplete preference information," European Journal of Operational Research, Elsevier, vol. 242(2), pages 568-579.

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