 Multi-objective ranking and selection: Optimal sampling laws and tractable approximations via SCOREEric A. Applegate, Guy Feldman, Susan R. Hunter and Raghu Pasupathy Journal of Simulation, 2020, vol. 14, issue 1, 21-40 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. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:tjsmxx:v:14:y:2020:i:1:p:21-40 Ordering information: This journal article can be ordered from DOI: 10.1080/17477778.2019.1633891 Access Statistics for this article Journal of Simulation is currently edited by Christine Currie More articles in Journal of Simulation from Taylor & Francis Journals |
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