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dc.contributor.authorWang, Weien_US
dc.date.accessioned2008-02-07T18:37:22Z
dc.date.available2008-02-07T18:37:22Z
dc.date.issued2007-08-17en_US
dc.identifier.urihttp://hdl.handle.net/1853/19784
dc.description.abstractSample average approximation (SAA) is a well-known solution methodology for traditional stochastic programs which are risk neutral in the sense that they consider optimization of expectation functionals. In this thesis we establish sample average approximation methods for two classes of non-traditional stochastic programs. The first class is that of stochastic min-max programs, i.e., min-max problems with expected value objectives, and the second class is that of expected value constrained stochastic programs. We specialize these SAA methods for risk-averse stochastic problems with a bi-criteria objective involving mean and mean absolute deviation, and those with constraints on conditional value-at-risk. For the proposed SAA methods, we prove that the results of the SAA problem converge exponentially fast to their counterparts for the true problem as the sample size increases. We also propose implementation schemes which return not only candidate solutions but also statistical upper and lower bound estimates on the optimal value of the true problem. We apply the proposed methods to solve portfolio selection and supply chain network design problems. Our computational results reflect good performance of the proposed SAA schemes. We also investigate the effect of various types of risk-averse stochastic programming models in controlling risk in these problems.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectExpected value constrained programen_US
dc.subjectMean absolute deviationen_US
dc.subjectConditional value-at-risken_US
dc.subjectPortfolio optimizationen_US
dc.subjectSupply chain network designen_US
dc.subjectStochastic min-max programen_US
dc.subject.lcshStochastic processes
dc.titleSample Average Approximation of Risk-Averse Stochastic Programsen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentIndustrial and Systems Engineeringen_US
dc.description.advisorCommittee Chair: Ahmed, Shabbir; Committee Member: Deng, Shijie; Committee Member: Li, Minqiang; Committee Member: Savelsbergh, Martin; Committee Member: Shapiro, Alexanderen_US


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