Stochastic optimization is vital to making soundengineering and business decisions under uncertainty.While the limited capability of handling complexdomain structures and random variables rendersanalytic methods helpless in many circumstances,stochastic optimization based on simulation is widelyapplicable. This work extends the traditionalresponse surface methodology into a surrogate modelframework to address high dimensional stochasticproblems. The framework integrates Latin hypercubesampling (LHS), domain reduction techniques, leastsquare support vector machine (LSSVM) and design &analysis of computer experiment (DACE) to buildsurrogate models that effectively captures domainstructures. In comparison with existing simulationbased optimization methods, the proposed frameworkleads to better solutions especially for problemswith high dimensions and high uncertainty. Thesurrogate model framework also demonstrates thecapability of addressing the curse-of-dimensionalityin stochastic dynamic risk optimization problems,where several important modification of the classicalBellman equation for stochastic dynamic problems(SDP) is also proposed.
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Xiaotao Wan, Ph.D: Studied Chemical Engineering at Tsinghua University and Purdue University with Focus on Supply Chain Optimization in Postgraduate Study. Supply Chain Consultant at Bayer Technology & Engineering (Shanghai) Co. Ltd.
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Wan XiaotaoXiaotao Wan, Ph.D: Studied Chemical Engineering at TsinghuanUniversity and Purdue University with Focus on Supply ChainnOptimization in Postgraduate Study. Supply Chain Consultant atnBayer Technology & Engineering (Shangha. Nº de ref. del artículo: 4961075
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Stochastic optimization is vital to making soundengineering and business decisions under uncertainty.While the limited capability of handling complexdomain structures and random variables rendersanalytic methods helpless in many circumstances,stochastic optimization based on simulation is widelyapplicable. This work extends the traditionalresponse surface methodology into a surrogate modelframework to address high dimensional stochasticproblems. The framework integrates Latin hypercubesampling (LHS), domain reduction techniques, leastsquare support vector machine (LSSVM) and design &analysis of computer experiment (DACE) to buildsurrogate models that effectively captures domainstructures. In comparison with existing simulationbased optimization methods, the proposed frameworkleads to better solutions especially for problemswith high dimensions and high uncertainty. Thesurrogate model framework also demonstrates thecapability of addressing the curse-of-dimensionalityin stochastic dynamic risk optimization problems,where several important modification of the classicalBellman equation for stochastic dynamic problems(SDP) is also proposed. Nº de ref. del artículo: 9783639140156
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Taschenbuch. Condición: Neu. Stochastic Optimization with Simulation Based Optimization | A Surrogate Model Framework | Xiaotao Wan | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639140156 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 101627433
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