A knowledge-based simulation optimization system with machine learning

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dc.contributor.authorCrouch, Ingrid W. M.en
dc.contributor.committeechairRees, Loren P.en
dc.contributor.committeememberGreenwood, Allen G.en
dc.contributor.committeememberRakes, Terry R.en
dc.contributor.committeememberSumichrast, Robert T.en
dc.contributor.committeememberTaylor, Bernard W. IIIen
dc.contributor.departmentAccounting and Information Systemsen
dc.date.accessioned2014-03-14T21:08:59Zen
dc.date.adate2006-02-01en
dc.date.available2014-03-14T21:08:59Zen
dc.date.issued1992-05-06en
dc.date.rdate2006-02-01en
dc.date.sdate2006-02-01en
dc.description.abstractA knowledge-based system is formulated to guide the search strategy selection process in simulation optimization. This system includes a framework for machine learning which enhances the knowledge base and thereby improves the ability of the system to guide optimizations. Response surfaces (i.e., the response of a simulation model to all possible input combinations) are first classified based on estimates of various surface characteristics. Then heuristics are applied to choose the most appropriate search strategy. As the search is carried out and more information about the surface becomes available, the knowledge-based system reclassifies the response surface and, if appropriate, selects a different search strategy. Periodically the system’s Learner is invoked to upgrade the knowledge base. Specifically, judgments are made to improve the heuristic knowledge (rules) in the knowledge base (i.e., rules are added, modified, or combined). The Learner makes these judgments using information from two sources. The first source is past experience -- all the information generated during previous simulation optimizations. The second source is results of experiments that the Learner performs to test hypotheses regarding rules in the knowledge base. The great benefits of simulation optimization (coupled with the high cost) have highlighted the need for efficient algorithms to guide the selection of search strategies. Earlier work in simulation optimization has led to the development of different search strategies for finding optimal-response-producing input levels. These strategies include response surface methodology, simulated annealing, random search, genetic algorithms, and single-factor search. Depending on the characteristics of the response surface (e.g., presence or absence of local optima, number of inputs, variance), some strategies can be more efficient and effective than others at finding an optimal solution. If the response surface were perfectly characterized, the most appropriate search strategy could, ideally, be immediately selected. However, characterization of the surface itself requires simulation runs. The knowledge-based system formulated here provides an effective approach to guiding search strategy selection in simulation optimization.en
dc.description.degreePh. D.en
dc.format.extentxi, 118 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-02012006-141728en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02012006-141728/en
dc.identifier.urihttp://hdl.handle.net/10919/37245en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1992.C768.pdfen
dc.relation.isformatofOCLC# 26176685en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1992.C768en
dc.subject.lcshComputer simulationen
dc.subject.lcshExpert systems (Computer science)en
dc.subject.lcshMachine learningen
dc.titleA knowledge-based simulation optimization system with machine learningen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineAccounting and Information Systemsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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