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dc.contributor.authorDoan, Xuan Vinh
dc.contributor.authorNatarajan, Karthik
dc.contributor.authorTeo, Chung-Piaw
dc.contributor.authorBertsimas, Dimitris J
dc.date.accessioned2012-04-04T15:25:39Z
dc.date.available2012-04-04T15:25:39Z
dc.date.issued2010-08
dc.date.submitted2009-04
dc.identifier.issn0364-765X
dc.identifier.issn1526-5471
dc.identifier.urihttp://hdl.handle.net/1721.1/69922
dc.description.abstractWe propose a semidefinite optimization (SDP) model for the class of minimax two-stage stochastic linear optimization problems with risk aversion. The distribution of second-stage random variables belongs to a set of multivariate distributions with known first and second moments. For the minimax stochastic problem with random objective, we provide a tight SDP formulation. The problem with random right-hand side is NP-hard in general. In a special case, the problem can be solved in polynomial time. Explicit constructions of the worst-case distributions are provided. Applications in a production-transportation problem and a single facility minimax distance problem are provided to demonstrate our approach. In our experiments, the performance of minimax solutions is close to that of data-driven solutions under the multivariate normal distribution and better under extremal distributions. The minimax solutions thus guarantee to hedge against these worst possible distributions and provide a natural distribution to stress test stochastic optimization problems under distributional ambiguity.en_US
dc.description.sponsorshipSingapore-MIT Alliance for Research and Technologyen_US
dc.description.sponsorshipNational University of Singapore. Dept. of Mathematicsen_US
dc.language.isoen_US
dc.publisherInstitute for Operations Research and the Management Sciencesen_US
dc.relation.isversionofhttp://dx.doi.org/10.1287/moor.1100.0445en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceProf. Bertsimas via Alex Caracuzzoen_US
dc.titleModels for Minimax Stochastic Linear Optimization Problems with Risk Aversionen_US
dc.typeArticleen_US
dc.identifier.citationBertsimas, D. et al. “Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion.” Mathematics of Operations Research 35.3 (2010): 580–602.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.approverBertsimas, Dimitris J.
dc.contributor.mitauthorDoan, Xuan Vinh
dc.contributor.mitauthorBertsimas, Dimitris J.
dc.relation.journalMathematics of Operations Researchen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsBertsimas, D.; Doan, X. V.; Natarajan, K.; Teo, C.-P.en
dc.identifier.orcidhttps://orcid.org/0000-0002-1985-1003
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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