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Multi-objective ranking and selection: Optimal sampling laws and tractable approximations via SCORE

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  • Eric A. Applegate
  • Guy Feldman
  • Susan R. Hunter
  • Raghu Pasupathy

Abstract

Consider the multi-objective ranking and selection (MORS) problem in which we select the Pareto-optimal set from a finite set of systems evaluated on three or more stochastic objectives. Toward determining how to allocate a simulation budget among the systems, we characterise the asymptotically optimal sample allocation that maximises the misclassification-probability decay rate, and we provide an implementable allocation called MO-SCORE. The MO-SCORE allocation simultaneously controls the probabilities of misclassification by exclusion and inclusion, identifies phantom Pareto systems crucial for computational efficiency, and models dependence between the objectives. The MO-SCORE allocation is fast and accurate for problems with three objectives or a small number of systems. For problems with four or more objectives and a large number of systems, we propose independent MO-SCORE (iMO-SCORE). Our numerical experience is extensive and promising: MO-SCORE and iMO-SCORE can be used to solve MORS problems involving several thousand systems in three and four objectives.

Suggested Citation

  • Eric A. Applegate & Guy Feldman & Susan R. Hunter & Raghu Pasupathy, 2020. "Multi-objective ranking and selection: Optimal sampling laws and tractable approximations via SCORE," Journal of Simulation, Taylor & Francis Journals, vol. 14(1), pages 21-40, January.
    • Handle: RePEc:taf:tjsmxx:v:14:y:2020:i:1:p:21-40
    DOI: 10.1080/17477778.2019.1633891
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    Cited by:

    1. 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.

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