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dc.contributor.advisorSchonfeld, Paul Men_US
dc.contributor.advisorChang, Gang-Lenen_US
dc.contributor.authorPark, Seong Jaeen_US
dc.date.accessioned2005-08-03T14:28:59Z
dc.date.available2005-08-03T14:28:59Z
dc.date.issued2005-05-02en_US
dc.identifier.urihttp://hdl.handle.net/1903/2496
dc.description.abstractThis thesis investigates the costs associated with a bus scheduling problem in an urban transit network for both deterministic and stochastic arrival processes and proposes computerized models for each. A simple genetic algorithm (SGA) with some problem-specific genetic operators is developed for the deterministic arrival process and a simulation-based genetic algorithm (SBGA) is developed for the stochastic arrival process. The new models are applied to an artificial bus network to test their efficiency. Several sensitivity analyses and a goodness test are conducted for each arrival process. The results show that the SGA model can find the optimized solution very quickly when it uses problem-specific operators such as the coordinated headway generator, coordinated headway crossover and coordinated headway mutation. They also show that the SBGA model can find a good solution even though it uses general genetic operators.en_US
dc.format.extent997607 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleBus Network Scheduling with Genetic Algorithms and Simulationen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentCivil Engineeringen_US
dc.subject.pqcontrolledTransportationen_US
dc.subject.pquncontrolledbus schedulingen_US
dc.subject.pquncontrolledmulti transfersen_US
dc.subject.pquncontrolledgenetic algorithmen_US


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