Permutation based Genetic Algorithm with Event-Scheduling/Time-Advance Algorithm as Decoder for a Flexible Job-shop Scheduling Problem

dc.contributor.advisorDefersha, Fantahun M.
dc.contributor.authorKo, C. H. Hayson
dc.date.accessioned2017-12-18T14:09:18Z
dc.date.available2017-12-18T14:09:18Z
dc.date.copyright2017-12
dc.date.created2017-12-13
dc.date.issued2017-12-18
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Applied Scienceen_US
dc.degree.programmeEngineeringen_US
dc.description.abstractToday, numerous research support the growing scheduling problems that exist globally in competitive businesses. Scheduling needs to become efficient in order to remain relevant against competitors. Simulations need to provide results in short periods of time so that adjustments can be made and unnecessary costs can be avoided. Scheduling problems have become larger in size and greater in complexity given the rising product variations and increase in variety for manufacturing equipment. Hence, there is a practical need for genetic algorithms solving scheduling problems to be fast and versatile. This thesis introduces an event-scheduling/time-advance algorithm for the decoder to reduce the load on the genetic algorithm with a smaller global search space. Consequently, convergence can be reached sooner and larger problems can be tackled easily. The structure of this heuristic algorithm allows metrics to be easily implemented in order to give the user performance measures on the scheduling problem.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada
dc.description.sponsorshipIntegrated Production and Manpower Scheduling
dc.description.sponsorshipMERSEN Canada Toronto Inc.
dc.identifier.urihttp://hdl.handle.net/10214/12091
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectschedulingen_US
dc.subjectgenetic algorithmen_US
dc.subjectflowshopen_US
dc.subjectjobshopen_US
dc.subjectflexibleen_US
dc.subjectevent schedulingen_US
dc.subjecttime advanceen_US
dc.subjectdecoderen_US
dc.subjectperformanceen_US
dc.subjectscalableen_US
dc.titlePermutation based Genetic Algorithm with Event-Scheduling/Time-Advance Algorithm as Decoder for a Flexible Job-shop Scheduling Problemen_US
dc.typeThesisen_US

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