Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets

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dc.contributorVirginia Techen
dc.contributor.authorHunter, S. R.en
dc.contributor.authorPasupathy, R.en
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessed2014-02-05en
dc.date.accessioned2014-03-05T14:00:22Zen
dc.date.available2014-03-05T14:00:22Zen
dc.date.issued2013en
dc.description.abstractConsider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.en
dc.description.sponsorshipOffice of Naval Research N000140810066, N000140910997, N000141110065en
dc.description.sponsorshipNational Science Foundation CMMI 0758441, CMMI 0800688en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSusan R. Hunter and Raghu Pasupathy. Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets. INFORMS Journal on Computing 2013 25:3, 527-542. doi: 10.1287/ijoc.1120.0519en
dc.identifier.doihttps://doi.org/10.1287/ijoc.1120.0519en
dc.identifier.issn1091-9856en
dc.identifier.urihttp://hdl.handle.net/10919/25826en
dc.identifier.urlhttp://pubsonline.informs.org/doi/pdf/10.1287/ijoc.1120.0519en
dc.language.isoenen
dc.publisherINFORMSen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectConstrained simulation optimizationen
dc.subjectOptimal allocationen
dc.subjectRanking anden
dc.subjectSelectionen
dc.subjectSystemsen
dc.titleOptimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Setsen
dc.title.serialInforms Journal on Computingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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