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dc.contributorVan Wunnik, Lucas Philippe
dc.contributor.authorPascual Poch, Mario
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Organització d'Empreses
dc.date.accessioned2020-01-15T11:04:17Z
dc.date.available2020-01-15T11:04:17Z
dc.date.issued2019-10-16
dc.identifier.urihttp://hdl.handle.net/2117/174846
dc.description.abstractIn this thesis we present a new interesting version of the mixed flow shop se-quencing problem, which at the same time is a version of the classic flow shop,a very common topic on operations research.We propose a genetic algorithm to solve it that we will compare at the endwith a simple initial genetic-based algorithm previously design. For that wefirst focus on the crossover operator as we consider it the most challenging parton a sequencing problem. We study and compare 5 different crossover operatorsand we choose the one that performs better. Finally we calibrate the populationsize, the weight of mutation and crossover operators on the algorithm and alsothe mutations operator itself.The goal of the thesis is to better understand the specific mixed flow shopproblem version presented and design a genetic algorithm that clearly improvesthe performance of the initial algorithm
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses
dc.subject.lcshHeuristic programming
dc.subject.lcshAlgorithms
dc.subject.lcshProgramming (Mathematics)
dc.titleA genetic algorithm for the mixed flow shop problem
dc.typeMaster thesis
dc.subject.lemacProgramació heurística
dc.subject.lemacAlgorismes
dc.subject.lemacProgramació (Matemàtica)
dc.identifier.slugETSEIB-240.143726
dc.rights.accessOpen Access
dc.date.updated2019-10-16T05:26:50Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria Industrial de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN ENGINYERIA INDUSTRIAL (Pla 2014)
dc.contributor.covenanteeUniversità degli studi di Trento


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