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dc.contributor.authorThiele, Aurélie*
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorMurat, Cécile*
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorGabrel, Virginie*
dc.date.accessioned2013-11-02T14:57:19Z
dc.date.available2013-11-02T14:57:19Z
dc.date.issued2014
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11974
dc.language.isoenen
dc.subjectDecision rulesen
dc.subjectRisk theoryen
dc.subjectDistributional robustnessen
dc.subjectRobust optimizationen
dc.subject.ddc003en
dc.titleRecent advances in robust optimization: An overviewen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherLehigh University, Industrial and Systems Engineering Department;États-Unis
dc.description.abstractenThis paper provides an overview of developments in robust optimization since 2007. It seeks to give a representative picture of the research topics most explored in recent years, highlight common themes in the investigations of independent research teams and highlight the contributions of rising as well as established researchers both to the theory of robust optimization and its practice. With respect to the theory of robust optimization, this paper reviews recent results on the cases without and with recourse, i.e., the static and dynamic settings, as well as the connection with stochastic optimization and risk theory, the concept of distributionally robust optimization, and findings in robust nonlinear optimization. With respect to the practice of robust optimization, we consider a broad spectrum of applications, in particular inventory and logistics, finance, revenue management, but also queueing networks, machine learning, energy systems and the public good. Key developments in the period from 2007 to present include: (i) an extensive body of work on robust decision-making under uncertainty with uncertain distributions, i.e., “robustifying” stochastic optimization, (ii) a greater connection with decision sciences by linking uncertainty sets to risk theory, (iii) further results on nonlinear optimization and sequential decision-making and (iv) besides more work on established families of examples such as robust inventory and revenue management, the addition to the robust optimization literature of new application areas, especially energy systems and the public good.en
dc.relation.isversionofjnlnameEuropean Journal of Operational Research
dc.relation.isversionofjnlvol235
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2014
dc.relation.isversionofjnlpages471-483
dc.relation.isversionofdoi10.1016/j.ejor.2013.09.036en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelRecherche opérationnelleen
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
hal.identifierhal-01495346*
hal.version1*
hal.update.actionupdateMetadata*
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