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A maximal covering location problem based optimization of complex processes : a novel computational approach

dc.contributor.advisorAzhmyakov, Vadim
dc.contributor.advisorPickl, Stefan
dc.contributor.authorFernández Gutiérrez, Juan Pablo
dc.coverage.spatialLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.date.accessioned2021-04-20T18:35:51Z
dc.date.available2021-04-20T18:35:51Z
dc.date.created2019-09-04
dc.identifier.otherCD-ROM 9019 2019
dc.identifier.urihttp://hdl.handle.net/11407/6338
dc.descriptionEsta tesis basada en artículos analiza un nuevo enfoque computacional para el problema de localización de cobertura máxima (MCLP, sigla en inglés). Consideramos una formulación de tipo difuso del MCLP genérico y desarrollamos los aspectos teóricos y numéricos necesarios del Método de Separación (SM) propuesto. Una estructura específica del MCLP originalmente dado hace posible reducirlo a dos problemas auxiliares de tipo mochila (Knapsack). La separación equivalente que proponemos reduce esencialmente la complejidad de los algoritmos resultantes. Este algoritmo también incorpora una técnica de relajación convencional y el método de escalarización aplicado a un problema auxiliar de optimización multiobjetivo. La metodología de solución propuesta se aplica a continuación a la optimización de la cadena de suministro en presencia de información incompleta. Estudiamos dos ejemplos ilustrativos y realizamos un análisis riguroso de los resultados obtenidos.
dc.description.abstractThis Ph.D. article-based thesis discusses a novel computational approach to the extended Maximal Covering Location Problem (MCLP). We consider a fuzzy-type formulation of the generic MCLP and develop the necessary theoretical and numerical aspects of the proposed Separation Method (SM). A speci_c structure of the originally given MCLP makes it possible to reduce it to two auxiliary Knapsack-type problems. The equivalent separation we propose reduces essentially the complexity of the resulting computational algorithms. This algorithm also incorporates a conventional relaxation technique and the scalarizing method applied to an auxiliary multiobjective optimization problem. The proposed solution methodology is next applied to Supply Chain optimization in the presence of incomplete information. We study two illustrative examples and give a rigorous analysis of the obtained results.
dc.format.extentp. 1-79
dc.format.mediumElectrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0
dc.titleA maximal covering location problem based optimization of complex processes : a novel computational approach
dc.rights.accessrightsinfo:eurepo/semantics/openAccess
dc.publisher.programDoctorado en Modelación y Computación Científica
dc.subject.lembAlgoritmos (Computadores)
dc.subject.lembComplejidad computacional
dc.subject.lembModelado
dc.subject.lembOptimización matemática
dc.relation.citationstartpage1
dc.relation.citationendpage79
dc.audienceComunidad Universidad de Medellín
dc.publisher.facultyFacultad de Ciencias Básicas
dc.publisher.placeMedellín
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dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTesis de Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.description.degreenameDoctor en Modelación y Computación Científica
dc.description.degreelevelDoctorado
dc.publisher.grantorUniversidad de Medellín


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