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dc.contributor.authorTaşgetiren, M. Fatih
dc.contributor.authorLiang, Yun-Chia
dc.contributor.authorŞevkli, Mehmet
dc.contributor.authorGençyılmaz, Güneş
dc.date.accessioned2016-04-19T08:34:12Z
dc.date.available2016-04-19T08:34:12Z
dc.date.issued2006-11-15
dc.identifier.urihttp://hdl.handle.net/11413/975
dc.description.abstractIn this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the variable neighbourhood search (VNS) is embedded in both algorithms to improve the solution quality and the computational efficiency. Ultimately, all the best known or optimal solutions of instances are found by the VNS version of both algorithms.tr_TR
dc.language.isoen_UStr_TR
dc.publisherTAYLOR & FRANCIS LTD, 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLANDtr_TR
dc.relationINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHtr_TR
dc.subjectparticle swarm optimizationtr_TR
dc.subjectdifferential evolutiontr_TR
dc.subjecttotal weighted tardinesstr_TR
dc.subjectsingle machine scheduling problemtr_TR
dc.subjectevolutionary algorithmstr_TR
dc.subjectscheduling problemtr_TR
dc.subjectsequencing problemstr_TR
dc.subjectbound algorithmtr_TR
dc.subjectsearchtr_TR
dc.subjectbranchtr_TR
dc.subjectneighborhoodtr_TR
dc.subjectminimizetr_TR
dc.subjectcoststr_TR
dc.subjectjobtr_TR
dc.subjectparçacık sürüsü optimizasyonutr_TR
dc.subjectdiferansiyel evrimtr_TR
dc.subjecttoplam ağırlıklı gecikmetr_TR
dc.subjecttek makine çizelgeleme problemitr_TR
dc.subjectevrimsel algoritmalartr_TR
dc.subjectzamanlama sorunutr_TR
dc.subjectsıralama problemleritr_TR
dc.subjectsınır algoritmasıtr_TR
dc.subjectaramatr_TR
dc.subjectşubetr_TR
dc.subjectkomşuluktr_TR
dc.subjectmaliyetlertr_TR
dc.subjecttr_TR
dc.titleParticle swarm optimization and differential evolution for the single machine total weighted tardiness problemtr_TR
dc.typeArticletr_TR
dc.contributor.authorIDTR43111tr_TR
dc.contributor.authorIDTR19428tr_TR
dc.contributor.authorIDTR30141tr_TR


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