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Finding the non-dominated Pareto set for multi-objective simulation models

Author

Listed:
  • Loo Lee
  • Ek Chew
  • Suyan Teng
  • David Goldsman

Abstract

This article considers a multi-objective Ranking and Selection (R+S) problem, where the system designs are evaluated in terms of more than one performance measure. The concept of Pareto optimality is incorporated into the R+S scheme, and attempts are made to find all of the non-dominated designs rather than a single “best” one. In addition to a performance index to measure how non-dominated a design is, two types of errors are defined to measure the probabilities that designs in the true Pareto/non-Pareto sets are dominated/non-dominated based on observed performance. Asymptotic allocation rules are derived for simulation replications based on a Lagrangian relaxation method, under the assumption that an arbitrarily large simulation budget is available. Finally, a simple sequential procedure is proposed to allocate the simulation replications based on the asymptotic allocation rules. Computational results show that the proposed solution framework is efficient when compared to several other algorithms in terms of its capability of identifying the Pareto set.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:9:p:656-674
    DOI: 10.1080/07408171003705367
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    Cited by:

    1. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
    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. Jiang, Jianlin & Lee, Loo Hay & Chew, Ek Peng & Gan, Chee Chun, 2015. "Port connectivity study: An analysis framework from a global container liner shipping network perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 73(C), pages 47-64.
    4. Kyle Cooper & Susan R. Hunter & Kalyani Nagaraj, 2020. "Biobjective Simulation Optimization on Integer Lattices Using the Epsilon-Constraint Method in a Retrospective Approximation Framework," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1080-1100, October.
    5. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    6. Xuanzhu Fan & Jiafu Tang & Chongjun Yan & Hainan Guo & Zhongfa Cao, 2021. "Outpatient appointment scheduling problem considering patient selection behavior: data modeling and simulation optimization," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 677-699, November.
    7. Demet Batur & Lina Wang & F. Fred Choobineh, 2018. "Methods for System Selection Based on Sequential Mean–Variance Analysis," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 724-738, November.
    8. Susan R. Hunter & Raghu Pasupathy, 2013. "Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 527-542, August.
    9. 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.
    10. Rojas Gonzalez, Sebastian & Jalali, Hamed & Van Nieuwenhuyse, Inneke, 2020. "A multiobjective stochastic simulation optimization algorithm," European Journal of Operational Research, Elsevier, vol. 284(1), pages 212-226.
    11. Yen-Yi Feng & I-Chin Wu & Tzu-Li Chen, 2017. "Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm," Health Care Management Science, Springer, vol. 20(1), pages 55-75, March.
    12. Lin, Rung-Chuan & Sir, Mustafa Y. & Pasupathy, Kalyan S., 2013. "Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services," Omega, Elsevier, vol. 41(5), pages 881-892.
    13. Joshua Q. Hale & Helin Zhu & Enlu Zhou, 2020. "Domination Measure: A New Metric for Solving Multiobjective Optimization," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 565-581, July.
    14. Xuanzhu Fan & Jiafu Tang & Chongjun Yan & Hainan Guo & Zhongfa Cao, 0. "Outpatient appointment scheduling problem considering patient selection behavior: data modeling and simulation optimization," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-23.
    15. 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.
    16. Waychal, Nachiketas & Laha, Arnab Kumar & Sinha, Ankur, 2022. "Customized forecasting with Adaptive Ensemble Generator," IIMA Working Papers WP 2022-06-04, Indian Institute of Management Ahmedabad, Research and Publication Department.

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