The optimization of simulation models by genetic algorithms:a comparative study

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dc.contributor.authorYunker, James M.en
dc.contributor.committeechairTew, Jeffrey D.en
dc.contributor.committeememberEyada, Osama K.en
dc.contributor.committeememberSumichrast, Robert T.en
dc.contributor.committeememberSchmidt, J. Williamen
dc.contributor.committeememberDeisenroth, Michael P.en
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2014-03-14T21:16:37Zen
dc.date.adate2008-07-28en
dc.date.available2014-03-14T21:16:37Zen
dc.date.issued1993en
dc.date.rdate2008-07-28en
dc.date.sdate2008-07-28en
dc.description.abstractThis dissertation is a comparative study of simulation optimization methods. We compare a new technique, genetic search, to two old techniques: the pattern search and the response surface methodology search. The pattern search uses the Hooke and Jeeves algorithm and the response surface method search uses the code of Dennis Smith. The research compares these three algorithms for accuracy and stability. In accuracy we look at how close the algorithm comes to the optimum. The optimum having been previously determined from exhaustive testing. We evaluate stability by using the variance of the response function as determined from 50 searches. The test-bed consists of three simulation models. We took the three simulation models from text books and modified them to make them optimization models if that was required. The first model consists of a big S, little s inventory system with two decision variables: big S and little s. The response is the monthly cost of operating the inventory system. The second model was a university time-sharing computer system with two decision variables: quantum, the amount of time that the computer spends on a job before sending it back to the queue and overhead, that is the time that its takes to execute this routing operation. The response was the cost of operating the system determined from a cost function. The third model was a job-shop with five decision variables: the number of machines at each of the five work stations. The response was the cost of operating the job-shop again determined from a cost function. The decision variables were integer for the inventory system and job-shop, and were real for the computer system.en
dc.description.degreePh. D.en
dc.format.extentxviii, 478 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-07282008-135041en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07282008-135041/en
dc.identifier.urihttp://hdl.handle.net/10919/38929en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1993.Y865.pdfen
dc.relation.isformatofOCLC# 30505599en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1993.Y865en
dc.subject.lcshAlgorithmsen
dc.subject.lcshMathematical optimizationen
dc.titleThe optimization of simulation models by genetic algorithms:a comparative studyen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineIndustrial and Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en
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