Stochastic Optimization with Simulation Based Optimization: A Surrogate Model Fr

Description: Stochastic Optimization with Simulation Based Optimization by Xiaotao Wan 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Stochastic optimization is vital to making sound engineering and business decisions under uncertainty. While the limited capability of handling complex domain structures and random variables renders analytic methods helpless in many circumstances, stochastic optimization based on simulation is widely applicable. This work extends the traditional response surface methodology into a surrogate model framework to address high dimensional stochastic problems. The framework integrates Latin hypercube sampling (LHS), domain reduction techniques, least square support vector machine (LSSVM) and design & analysis of computer experiment (DACE) to build surrogate models that effectively captures domain structures. In comparison with existing simulation based optimization methods, the proposed framework leads to better solutions especially for problems with high dimensions and high uncertainty. The surrogate model framework also demonstrates the capability of addressing the curse-of-dimensionality in stochastic dynamic risk optimization problems, where several important modification of the classical Bellman equation for stochastic dynamic problems (SDP) is also proposed. Author Biography Xiaotao Wan, Ph.D: Studied Chemical Engineering at TsinghuaUniversity and Purdue University with Focus on Supply ChainOptimization in Postgraduate Study. Supply Chain Consultant atBayer Technology & Engineering (Shanghai) Co. Ltd. Details ISBN363914015X Author Xiaotao Wan Short Title STOCHASTIC OPTIMIZATION W/SIMU Pages 136 Publisher VDM Verlag Language English ISBN-10 363914015X ISBN-13 9783639140156 Media Book Format Paperback Year 2009 Subtitle A Surrogate Model Framework UK Release Date 2009-04-15 Imprint VDM Verlag Country of Publication Germany Illustrations Illustrations, black and white Publication Date 2009-04-15 Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:132421137;

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