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dc.contributor.authorBertsimas, Dimitris J.
dc.contributor.authorDunning, Iain Robert
dc.contributor.authorLubin, Miles C.
dc.date.accessioned2016-06-14T15:53:14Z
dc.date.available2016-06-14T15:53:14Z
dc.date.issued2015-07
dc.date.submitted2014-03
dc.identifier.issn1619-697X
dc.identifier.issn1619-6988
dc.identifier.urihttp://hdl.handle.net/1721.1/103105
dc.description.abstractRobust optimization (RO) is a tractable method to address uncertainty in optimization problems where uncertain parameters are modeled as belonging to uncertainty sets that are commonly polyhedral or ellipsoidal. The two most frequently described methods in the literature for solving RO problems are reformulation to a deterministic optimization problem or an iterative cutting-plane method. There has been limited comparison of the two methods in the literature, and there is no guidance for when one method should be selected over the other. In this paper we perform a comprehensive computational study on a variety of problem instances for both robust linear optimization (RLO) and robust mixed-integer optimization (RMIO) problems using both methods and both polyhedral and ellipsoidal uncertainty sets. We consider multiple variants of the methods and characterize the various implementation decisions that must be made. We measure performance with multiple metrics and use statistical techniques to quantify certainty in the results. We find for polyhedral uncertainty sets that neither method dominates the other, in contrast to previous results in the literature. For ellipsoidal uncertainty sets we find that the reformulation is better for RLO problems, but there is no dominant method for RMIO problems. Given that there is no clearly dominant method, we describe a hybrid method that solves, in parallel, an instance with both the reformulation method and the cutting-plane method. We find that this hybrid approach can reduce runtimes to 50–75 % of the runtime for any one method and suggest ways that this result can be achieved and further improved on.en_US
dc.description.sponsorshipUnited States. Office of Naval Research (ONR Grant N00014-12-1-0999)en_US
dc.description.sponsorshipUnited States. Dept. of Energy (DOE Computational Science Graduate Fellowship, Grant No. DE-FG02-97ER25308)en_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10287-015-0236-zen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer Berlin Heidelbergen_US
dc.titleReformulation versus cutting-planes for robust optimizationen_US
dc.typeArticleen_US
dc.identifier.citationBertsimas, Dimitris, Iain Dunning, and Miles Lubin. “Reformulation Versus Cutting-Planes for Robust Optimization.” Comput Manag Sci 13, no. 2 (July 21, 2015): 195–217.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.mitauthorBertsimas, Dimitris J.en_US
dc.contributor.mitauthorDunning, Iain Roberten_US
dc.contributor.mitauthorLubin, Miles C.en_US
dc.relation.journalComputational Management Scienceen_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
dc.date.updated2016-05-23T12:11:45Z
dc.language.rfc3066en
dc.rights.holderSpringer-Verlag Berlin Heidelberg
dspace.orderedauthorsBertsimas, Dimitris; Dunning, Iain; Lubin, Milesen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0001-6721-5506
dc.identifier.orcidhttps://orcid.org/0000-0002-1985-1003
dc.identifier.orcidhttps://orcid.org/0000-0001-6781-9633
mit.licenseOPEN_ACCESS_POLICYen_US


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