Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/460
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dc.contributor.authorStanimirović, Zoricaen_US
dc.date.accessioned2022-08-13T09:28:38Z-
dc.date.available2022-08-13T09:28:38Z-
dc.date.issued2007-01-01-
dc.identifier.isbn9789812709691-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/460-
dc.description.abstractThe aim of this study is to present a Genetic Algorithm (GA) for solving the Capacitated Single Allocation Hub Location Problem (CSAHLP) and to deiiionstrate its robustness aiid effectiveness for solving this problem. The apprcpriate objective function is correcting infeasible individuals to be feasible. These corrections are frequent in the initial population, so that in the subsequent generations genetic operators slightly violate the feasibility of individuals and the necessary corrections are rare. The solutions of proposed GA method are compared with the best solutions presented in the literature by considering various problem sizes of the AP data set.en_US
dc.subjectCapacitated hub location problemsen_US
dc.subjectDiscrete locationen_US
dc.subjectEvolutionary computationen_US
dc.subjectGenetic algorithmsen_US
dc.titleSolving the capacitated single allocation hub location problem using genetic algorithmen_US
dc.typeBook Parten_US
dc.relation.publicationRecent Advances in Stochastic Modeling and Data Analysisen_US
dc.identifier.doi10.1142/9789812709691_0055-
dc.identifier.scopus2-s2.0-84967495587-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84967495587-
dc.contributor.affiliationNumerical Mathematics and Optimizationen_US
dc.relation.isbn978-981-270-968-4en_US
dc.relation.firstpage464en_US
dc.relation.lastpage471en_US
item.fulltextNo Fulltext-
item.openairetypeBook Part-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.deptNumerical Mathematics and Optimization-
crisitem.author.orcid0000-0001-5658-4111-
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