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dc.creatorCarrizosa Priego, Emilio Josées
dc.creatorOlivares Nadal, Alba Victoriaes
dc.creatorRamírez Cobo, Josefaes
dc.date.accessioned2016-06-27T10:48:53Z
dc.date.available2016-06-27T10:48:53Z
dc.date.issued2016-04
dc.identifier.citationCarrizosa Priego, E.J., Olivares Nadal, A.V. y Ramírez Cobo, J. (2016). Robust newsvendor problem with autoregressive demand. Computers and Operations Research, 68 (C), 123-133.
dc.identifier.issn0305-0548es
dc.identifier.issn1873-765Xes
dc.identifier.urihttp://hdl.handle.net/11441/42771
dc.description.abstractThis paper explores the classic single-item newsvendor problem under a novel setting which combines temporal dependence and tractable robust optimization. First, the demand is modeled as a time series which follows an autoregressive process AR(p), p ≥ 1. Second, a robust approach to maximize the worst-case revenue is proposed: a robust distribution-free autoregressive forecasting method, which copes with non-stationary time series, is formulated. A closed-form expression for the optimal solution is found for the problem for p = 1; for the remaining values of p, the problem is expressed as a nonlinear convex optimization program, to be solved numerically. The optimal solution under the robust method is compared with those obtained under two versions of the classic approach, in which either the demand distribution is unknown, and assumed to have no autocorrelation, or it is assumed to follow an AR(p) process with normal error terms. Numerical experiments show that our proposal usually outperforms the previous benchmarks, not only with regard to robustness, but also in terms of the average revenue.es
dc.description.sponsorshipMinisterio de Economía y Competitividades
dc.description.sponsorshipJunta de Andalucíaes
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofComputers and Operations Research, 68 (C), 123-133.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDistribution-free newsboy problemes
dc.subjectAutoregressive processes
dc.subjectUncertainty setes
dc.subjectMinimaxes
dc.subjectRobust optimizationes
dc.subjectForecastinges
dc.titleRobust newsvendor problem with autoregressive demandes
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/MTM2012-36163es
dc.relation.projectIDP11-FQM-7603es
dc.relation.projectIDFQM-329es
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.cor.2015.11.002es
dc.identifier.doi10.1016/j.cor.2015.11.002es
dc.contributor.groupUniversidad de Sevilla. FQM329: Optimizaciones
idus.format.extent35 p.es
dc.journaltitleComputers and Operations Researches
dc.publication.volumen68es
dc.publication.issueCes
dc.publication.initialPage123es
dc.publication.endPage133es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/42771
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). España
dc.contributor.funderJunta de Andalucía

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