Genetic local search for multi-objective flowshop scheduling problems

dc.creatorArroyo, JEC
dc.creatorArmentano, VA
dc.date2005
dc.dateDEC 16
dc.date2014-11-13T16:03:28Z
dc.date2015-11-26T18:08:07Z
dc.date2014-11-13T16:03:28Z
dc.date2015-11-26T18:08:07Z
dc.date.accessioned2018-03-29T00:50:13Z
dc.date.available2018-03-29T00:50:13Z
dc.identifierEuropean Journal Of Operational Research. Elsevier Science Bv, v. 167, n. 3, n. 717, n. 738, 2005.
dc.identifier0377-2217
dc.identifierWOS:000231298300008
dc.identifier10.1016/j.ejor.2004.07.017
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/67924
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/67924
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/67924
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1293747
dc.descriptionThis paper addresses flowshop scheduling problems with multiple performance criteria in such a way as to provide the decision maker with approximate Pareto optimal solutions. Genetic algorithms have attracted the attention of researchers in the nineties as a promising technique for solving multi-objective combinatorial optimization problems. We propose a genetic local search algorithm with features such as preservation of dispersion in the population, elitism, and use of a parallel multi-objective local search so as intensify the search in distinct regions. The concept of Pareto dominance is used to assign fitness to the solutions and in the local search procedure. The algorithm is applied to the flowshop scheduling problem for the following two pairs of objectives: (i) makespan and maximum tardiness; (ii) makespan and total tardiness. For instances involving two machines, the algorithm is compared with Branchand-Bound algorithms proposed in the literature. For such instances and larger ones, involving up to 80 jobs and 20 machines, the performance of the algorithm is compared with two multi-objective genetic local search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set. (C) 2004 Elsevier B.V. All rights reserved.
dc.description167
dc.description3
dc.description717
dc.description738
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationEuropean Journal Of Operational Research
dc.relationEur. J. Oper. Res.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectmulti-objective combinatorial optimization
dc.subjectmetaheuristics
dc.subjectgenetic local search
dc.subjectflowshop scheduling
dc.subjectTabu Search
dc.subject2-machine Flowshop
dc.subjectCombinatorial Optimization
dc.subjectSingle-machine
dc.subjectAlgorithm
dc.subjectMultiple
dc.subjectBicriteria
dc.subjectHeuristics
dc.subjectSet
dc.subjectObjectives
dc.titleGenetic local search for multi-objective flowshop scheduling problems
dc.typeArtículos de revistas


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