Please use this identifier to cite or link to this item: https://doi.org/10.1287/opre.1090.0795
DC FieldValue
dc.titleDistributionally robust optimization and its tractable approximations
dc.contributor.authorGoh, J.
dc.contributor.authorSim, M.
dc.date.accessioned2013-10-09T03:24:32Z
dc.date.available2013-10-09T03:24:32Z
dc.date.issued2010
dc.identifier.citationGoh, J., Sim, M. (2010). Distributionally robust optimization and its tractable approximations. Operations Research 58 (4 PART 1) : 902-917. ScholarBank@NUS Repository. https://doi.org/10.1287/opre.1090.0795
dc.identifier.issn0030364X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/44009
dc.description.abstractIn this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds. © 2010 INFORMS.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1287/opre.1090.0795
dc.sourceScopus
dc.subjectProgramming
dc.subjectStochastic
dc.typeArticle
dc.contributor.departmentDECISION SCIENCES
dc.description.doi10.1287/opre.1090.0795
dc.description.sourcetitleOperations Research
dc.description.volume58
dc.description.issue4 PART 1
dc.description.page902-917
dc.description.codenOPREA
dc.identifier.isiut000280786400010
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