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dc.contributorCervelló Pastor, Cristina
dc.contributor.authorGarrisi, Gianmarco
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica
dc.date.accessioned2019-10-03T12:56:01Z
dc.date.available2020-10-04T00:27:22Z
dc.date.issued2019-07
dc.identifier.urihttp://hdl.handle.net/2117/169126
dc.descriptionGenerating a railway timetable is known to be an NP-hard problem. While scheduling few trains is easy, complications quickly explode with increasing number of trains. The most important factors contributing to complexity are interdepencies between trains (such as connections) and the inability of trains to overtake one another on the same track.
dc.description.abstractIn this work, a mathematical formulation of the train scheduling problem is provided as a Mixed Integer Linear Program. The MILP can be solved for easy cases, but computation time makes it impractical for more complex examples. Then a genetic algorithm is employed in the solution of the problem, with heuristic techniques to generate an initial population. The algorithm is applied to a number of problem instances producing feasible, though not optimal, solutions in less then one minute. Some improvements are suggested to obtain better results and further improve computation time.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshGenetic algorithms
dc.subject.lcshRailroads
dc.subject.othergenetic algorithm
dc.subject.othertrain scheduling
dc.subject.otherMILP
dc.subject.otherheuristic
dc.titleModel and optimize train scheduling on railways network
dc.typeMaster thesis
dc.subject.lemacAlgorismes genètics
dc.subject.lemacFerrocarrils
dc.identifier.slugETSETB-230.146298
dc.rights.accessOpen Access
dc.date.updated2019-07-29T05:51:17Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN ENGINYERIA DE TELECOMUNICACIÓ (Pla 2013)


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