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Article: Distributionally Robust Selection of the Best

TitleDistributionally Robust Selection of the Best
Authors
Keywordsselection of the best
distributional robustness
input uncertainty
probability of correct selection
Issue Date2020
PublisherINFORMS. The Journal's web site is located at http://mansci.pubs.informs.org
Citation
Management Science, 2020, v. 66 n. 1, p. 190-208 How to Cite?
AbstractSpecifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the presence of such input uncertainty. We model such uncertainty by an ambiguity set consisting of a finite number of plausible input distributions and aim to select the alternative with the best worst-case mean performance over the ambiguity set. We refer to this problem as robust selection of the best (RSB). To solve the RSB problem, we develop a two-stage selection procedure and a sequential selection procedure; we then prove that both procedures can achieve at least a user-specified probability of correct selection under mild conditions. Extensive numerical experiments are conducted to investigate the computational efficiency of the two procedures. Finally, we apply the RSB approach to study a queueing system’s staffing problem using synthetic data and an appointment-scheduling problem using real data from a large hospital in China. We find that the RSB approach can generate decisions significantly better than other widely used approaches.
Persistent Identifierhttp://hdl.handle.net/10722/272684
ISSN
2023 Impact Factor: 4.6
2023 SCImago Journal Rankings: 5.438
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFan, W-
dc.contributor.authorHong, LJ-
dc.contributor.authorZhang, X-
dc.date.accessioned2019-08-06T09:14:36Z-
dc.date.available2019-08-06T09:14:36Z-
dc.date.issued2020-
dc.identifier.citationManagement Science, 2020, v. 66 n. 1, p. 190-208-
dc.identifier.issn0025-1909-
dc.identifier.urihttp://hdl.handle.net/10722/272684-
dc.description.abstractSpecifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the presence of such input uncertainty. We model such uncertainty by an ambiguity set consisting of a finite number of plausible input distributions and aim to select the alternative with the best worst-case mean performance over the ambiguity set. We refer to this problem as robust selection of the best (RSB). To solve the RSB problem, we develop a two-stage selection procedure and a sequential selection procedure; we then prove that both procedures can achieve at least a user-specified probability of correct selection under mild conditions. Extensive numerical experiments are conducted to investigate the computational efficiency of the two procedures. Finally, we apply the RSB approach to study a queueing system’s staffing problem using synthetic data and an appointment-scheduling problem using real data from a large hospital in China. We find that the RSB approach can generate decisions significantly better than other widely used approaches.-
dc.languageeng-
dc.publisherINFORMS. The Journal's web site is located at http://mansci.pubs.informs.org-
dc.relation.ispartofManagement Science-
dc.subjectselection of the best-
dc.subjectdistributional robustness-
dc.subjectinput uncertainty-
dc.subjectprobability of correct selection-
dc.titleDistributionally Robust Selection of the Best-
dc.typeArticle-
dc.identifier.emailZhang, X: xiaoweiz@hku.hk-
dc.identifier.authorityZhang, X=rp02554-
dc.description.naturepostprint-
dc.identifier.doi10.1287/mnsc.2018.3213-
dc.identifier.scopuseid_2-s2.0-85074585781-
dc.identifier.hkuros299670-
dc.identifier.hkuros306386-
dc.identifier.hkuros312155-
dc.identifier.volume66-
dc.identifier.issue1-
dc.identifier.spage190-
dc.identifier.epage208-
dc.identifier.isiWOS:000507342700010-
dc.publisher.placeUnited States-
dc.identifier.issnl0025-1909-

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