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Simulation-based Stochastic Optimization on Discrete Domains: Integrating Optimal Computing and Response Surfaces

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dc.contributor.advisor Chen, Chun-Hung
dc.contributor.author Brantley, Mark W
dc.creator Brantley, Mark W
dc.date 2011-03-31
dc.date.accessioned 2011-05-25T15:18:16Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2011-05-25T15:18:16Z
dc.date.issued 2011-05-25
dc.identifier.uri http://hdl.handle.net/1920/6347
dc.description.abstract Simulation can be a very powerful tool to help decision making in many applications but exploring multiple courses of actions can be time consuming. Numerous ranking & selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. This dissertation explores the potential of further enhancing R&S efficiency by incorporating simulation information from across the domain into a regression metamodel. Under some common conditions in most regression-based approaches, our new method provides approximately optimal rules that determine the design locations to conduct simulation runs and the number of samples allocated to each design location for problems with only one partition. In addition to utilizing concepts from the design of experiments (DOE) literature, it introduces the probability of correct selection (PCS) optimality criterion that underpins our new R&S method to the DOE literature. This dissertation then extends the method by incorporating simulation information from across a partitioned domain into a regression based metamodel. Our new method provides approximately optimal rules for between and within partitions that determine the number of samples allocated to each design location. Numerical experiments demonstrate that our new approaches for one partition domains and for multiple partition domains can dramatically enhance efficiency over existing efficient R&S methods. en_US
dc.language.iso en_US en_US
dc.subject Stochastic Optimization en_US
dc.subject Optimal Computing en_US
dc.subject Response Surfaces en_US
dc.subject Design of Experiments en_US
dc.subject discrete Event Stimulation en_US
dc.subject Optimal Simulation Design en_US
dc.title Simulation-based Stochastic Optimization on Discrete Domains: Integrating Optimal Computing and Response Surfaces en_US
dc.type Dissertation en
thesis.degree.name PhD in Information Technology en_US
thesis.degree.level Doctoral en
thesis.degree.discipline Information Technology en
thesis.degree.grantor George Mason University en


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