Regret in the Newsvendor Model with Demand and Yield Randomness

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dc.contributor.authorChen, Zhien
dc.contributor.authorXie, Weijunen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2021-09-24T23:48:24Zen
dc.date.available2021-09-24T23:48:24Zen
dc.date.issued2021-07-28en
dc.date.updated2021-09-24T23:48:19Zen
dc.description.abstractWe study the fundamental stochastic newsvendor model that considers both demand and yield randomness. It is usually difficult in practice to describe precisely the joint demand and yield distribution, although partial statistical information and empirical data about this ambiguous distribution are often accessible. We combat the issue of distributional ambiguity by taking a data-driven distributionally robust optimization approach to hedge against all distributions that are sufficiently close to a uniform and discrete distribution of empirical data, where closeness is measured by the type-∞ Wasserstein distance. We adopt the minimax regret decision criterion to assess the optimal order quantity that minimizes the worst-case regret. Several properties about the minimax regret model, including optimality condition, regret bound, and the worst-case distribution, are presented. The optimal order quantity can be determined via an efficient golden section search. We extend the analysis to the Hurwicz criterion model, which generalizes the popular albeit pessimistic maximin model (maximizing the worst-case expected profit) and its (less noticeable) more optimistic counterpart—the maximax model (maximizing the best-case expected profit). Finally, we present numerical comparisons of our data-driven minimax regret model with data-driven models based on the Hurwicz criterion and with a minimax regret model based on partial statistical information on moments.en
dc.description.versionAccepted versionen
dc.format.extent22 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierpoms.13515 (Article number)en
dc.identifier.doihttps://doi.org/10.1111/poms.13515en
dc.identifier.eissn1937-5956en
dc.identifier.issn1059-1478en
dc.identifier.orcidXie, Weijun [0000-0001-5157-1194]en
dc.identifier.urihttp://hdl.handle.net/10919/105060en
dc.language.isoenen
dc.publisherWileyen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000678204700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectEngineering, Manufacturingen
dc.subjectOperations Research & Management Scienceen
dc.subjectEngineeringen
dc.subjectdemand randomnessen
dc.subjectyield randomnessen
dc.subjectminimax regreten
dc.subjectHurwicz criterionen
dc.subjecttype-infinity Wasserstein distanceen
dc.subjectdata-driven decision-making under uncertaintyen
dc.subjectDISTRIBUTIONALLY ROBUST OPTIMIZATIONen
dc.subjectSUPPLY CHAINSen
dc.subjectRISKen
dc.subjectBOUNDSen
dc.subjectREFORMULATIONSen
dc.subjectUNCERTAINTYen
dc.subjectAMBIGUITYen
dc.subjectPOLICIESen
dc.subject0102 Applied Mathematicsen
dc.subject1503 Business and Managementen
dc.subjectOperations Researchen
dc.titleRegret in the Newsvendor Model with Demand and Yield Randomnessen
dc.title.serialProducation and Operations Managementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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