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dc.creatorAzhmyakov V.spa
dc.creatorFernández-Gutiérrez J.P.spa
dc.creatorGadi S.K.spa
dc.creatorPickl S.spa
dc.date.accessioned2017-12-19T19:36:52Z
dc.date.available2017-12-19T19:36:52Z
dc.date.created2016spa
dc.identifier.issn24058963spa
dc.identifier.urihttp://hdl.handle.net/11407/4379
dc.description.abstractThis paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the originally given MCLP to two auxiliary optimization Knapsack-type problems. The equivalent transformation (separation) we propose provides a useful tool for an effective numerical treatment of the original MCLP and reduces the complexity of algorithms. The computational methodology we follow involves a specific Lagrange relaxation procedure. We give a rigorous formal analysis of the resulting algorithm and apply it to a practically oriented example of an optimal RSCMS design. © 2016eng
dc.language.isoengspa
dc.publisherElsevier B.V.spa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85012868634&doi=10.1016%2fj.ifacol.2016.12.175&partnerID=40&md5=26ac509f85a752096da4a6e849f29c78spa
dc.sourceScopusspa
dc.sourcereponame:Repositorio Institucionalspa
dc.sourceinstname:Universidad de Medellínspa
dc.titleA Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimizationspa
dc.typeArticlespa
dc.typeinfo:eu-repo/semantics/publishedVersionspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessspa
dc.contributor.affiliationAzhmyakov, V., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, Colombiaspa
dc.contributor.affiliationFernández-Gutiérrez, J.P., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, Colombiaspa
dc.contributor.affiliationGadi, S.K., Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Coahuila, Torreon, Mexicospa
dc.contributor.affiliationPickl, S., Department of Computer Science, Universität der Bundeswehr München, München, Germanyspa
dc.identifier.doi10.1016/j.ifacol.2016.12.175spa
dc.subject.keywordComputational complexityeng
dc.subject.keywordInteger programmingeng
dc.subject.keywordSupply chain managementeng
dc.subject.keywordComplexity of algorithmeng
dc.subject.keywordComputational methodologyeng
dc.subject.keywordEquivalent transformationseng
dc.subject.keywordIncomplete informationeng
dc.subject.keywordMaximal covering location problems (MCLP)eng
dc.subject.keywordNumerical approacheseng
dc.subject.keywordSupply chain management systemeng
dc.subject.keywordSupply chain optimizationeng
dc.subject.keywordOptimizationeng
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.abstractThis paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the originally given MCLP to two auxiliary optimization Knapsack-type problems. The equivalent transformation (separation) we propose provides a useful tool for an effective numerical treatment of the original MCLP and reduces the complexity of algorithms. The computational methodology we follow involves a specific Lagrange relaxation procedure. We give a rigorous formal analysis of the resulting algorithm and apply it to a practically oriented example of an optimal RSCMS design. © 2016eng
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dc.creator.affiliationDepartamento de Ciencias Basicas, Universidad de Medellin, Medellin, Colombiaspa
dc.creator.affiliationFacultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Coahuila, Torreon, Mexicospa
dc.creator.affiliationDepartment of Computer Science, Universität der Bundeswehr München, München, Germanyspa
dc.relation.ispartofesIFAC-PapersOnLinespa
dc.relation.ispartofesIFAC-PapersOnLine Volume 49, Issue 31, 2016, Pages 137-142spa


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