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Efficient comparison of constrained systems using dormancy

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  • Healey, Christopher M.
  • Andradóttir, Sigrún
  • Kim, Seong-Hee

Abstract

We consider the problem of finding the best simulated system under a primary performance measure, while also satisfying stochastic constraints on secondary performance measures. We improve upon existing constrained selection procedures by allowing certain systems to become dormant, halting sampling for those systems as the procedure continues. A system goes dormant when it is found inferior to another system whose feasibility has not been determined, and returns to contention only if its superior system is eliminated. If found feasible, the superior system will eliminate the dormant system. By making systems dormant, we avoid collecting unnecessary observations from inferior systems. The paper also proposes other modifications, and studies the impact and benefits of our approaches (compared to similar constrained selection procedures) through experimental results and asymptotic approximations. Additionally, we discuss the difficulties associated with procedures that use sample means of unequal, random sample sizes, which commonly occurs within constrained selection and optimization-via-simulation.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:224:y:2013:i:2:p:340-352
    DOI: 10.1016/j.ejor.2012.08.012
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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. Seong-Hee Kim & Barry L. Nelson, 2006. "On the Asymptotic Validity of Fully Sequential Selection Procedures for Steady-State Simulation," Operations Research, INFORMS, vol. 54(3), pages 475-488, June.
    3. 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.
    4. 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.
    5. Loo Lee & Ek Chew & Suyan Teng & David Goldsman, 2010. "Finding the non-dominated Pareto set for multi-objective simulation models," IISE Transactions, Taylor & Francis Journals, vol. 42(9), pages 656-674.
    6. 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.
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    Cited by:

    1. Lee, Mi Lim & Park, Chuljin & Park, Dong Uk, 2018. "Self-adjusting the tolerance level in a fully sequential feasibility check procedure," European Journal of Operational Research, Elsevier, vol. 271(2), pages 733-745.
    2. Yuwei Zhou & Sigrún Andradóttir & Seong-Hee Kim & Chuljin Park, 2022. "Finding Feasible Systems for Subjective Constraints Using Recycled Observations," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3080-3095, November.
    3. L. Jeff Hong & Jun Luo & Barry L. Nelson, 2015. "Chance Constrained Selection of the Best," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 317-334, May.

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