Application of genetic algorithm to mixed-model assembly line balancing

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dc.contributor.authorEvans, Jonathan D.en
dc.contributor.committeechairEyada, Osama K.en
dc.contributor.committeememberSarin, Subhash C.en
dc.contributor.committeememberSumichrast, Robert T.en
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2014-03-14T21:52:49Zen
dc.date.adate2008-12-30en
dc.date.available2014-03-14T21:52:49Zen
dc.date.issued1996-03-05en
dc.date.rdate2008-12-30en
dc.date.sdate2008-12-30en
dc.description.abstractThe demand for increased diversity, reduced cycle time, and reduced work-in-process has caused increased popularity of mixed-model assembly lines. These lines combine the productivity of an assembly line and the flexibility of a job shop. The mixed-model assembly line allows setup time between models to be zero. Large lines mixed-model assembly lines require a timely, near-optimal method. A well balanced line reduces worker idle time and simplifies the mixed-model assembly line sequencing problem. Prior attempts to solve the balancing problem have been in-adequate. Heuristic techniques are too simple to find near-optimal solutions and yield only one solution. An exhaustive search requires too much processing time. Simulated Annealing works well, but yields only one solution per run and the solutions may vary because of the random nature of the Simulated Annealing process. Multiple runs are required to get more than one solution, each run requiring some amount of time which depends on problem size. If only one run is performed, the solution achieved may be far from optimal. In addition, Simulated Annealing requires different parameters depending on the size of the problem. The Genetic Algorithm (GA) is a probabilistic heuristic search strategy. In most cases, it begins with a population of random solutions. Then the population is reproduced using crossover and mutation with the fittest solutions having a higher probability of being parents. The idea is survival of the fittest, poor or unfit solutions do not reproduce and are replaced by better or fitter solutions. The final generation should yield multiple near optimal solutions. The objective of this study is to investigate the Genetic Algorithm and its performance compared to Simulated Annealing for large mixed-model assembly lines. The results will show that the Genetic Algorithm will perform comparably to the Simulated Annealing. The Genetic Algorithm will be used to solve various mixed-model assembly line problems to discover the correct parameters to solve any mixed-model assembly line balancing problem.en
dc.description.degreeMaster of Scienceen
dc.format.extentviii, 89 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-12302008-063806en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12302008-063806/en
dc.identifier.urihttp://hdl.handle.net/10919/46466en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V855_1996.E936.pdfen
dc.relation.isformatofOCLC# 34793094en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectgenetic algorithmen
dc.subjectmixed-model assembly line balancingen
dc.subject.lccLD5655.V855 1996.E936en
dc.titleApplication of genetic algorithm to mixed-model assembly line balancingen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplineIndustrial and Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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