Genetic algorithm, MIP and improvement heuristic applied to the MLCLP with backlogging

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:30:11Z
dc.date.accessioned2022-10-05T18:57:27Z
dc.date.available2014-05-27T11:30:11Z
dc.date.available2022-10-05T18:57:27Z
dc.date.created2014-05-27T11:30:11Z
dc.date.issued2013-08-21
dc.identifier2013 IEEE Congress on Evolutionary Computation, CEC 2013, p. 1483-1490.
dc.identifierhttp://hdl.handle.net/11449/76310
dc.identifier10.1109/CEC.2013.6557738
dc.identifier2-s2.0-84881575854
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3925201
dc.description.abstractThe present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches. © 2013 IEEE.
dc.languageeng
dc.relation2013 IEEE Congress on Evolutionary Computation, CEC 2013
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectgenetic algorithm
dc.subjecthybrid metaheuristic
dc.subjectlot-sizing
dc.subjectmulti-level
dc.subjectCapacitated lot sizing problem
dc.subjectComputational results
dc.subjectHybrid Meta-heuristic
dc.subjectLot sizing
dc.subjectMixed-Integer Programming
dc.subjectBenchmarking
dc.subjectHeuristic methods
dc.subjectInteger programming
dc.subjectGenetic algorithms
dc.titleGenetic algorithm, MIP and improvement heuristic applied to the MLCLP with backlogging
dc.typeTrabalho apresentado em evento


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