A New Approach to the Optimization of the CVRP through Genetic Algorithms

Autores
Frutos, Mariano; Tohmé, Fernando Abel
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a new approach to the analysis of complex distribution problems under capacity constraints. These problems are known in the literature as CVRPs (Capacitated Vehicle Routing Problems). The procedure introduced in this paper optimizes a transformed variant of a CVRP. It starts generating feasible clusters and codifies their ordering. In the next stage the procedure feeds this information into a genetic algorithm for its optimization. This makes the algo-rithm independent of the constraints and improves its performance. Van Breedam problems have been used to test this technique. While the results obtained are similar to those in other works, the processing times are longer.
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
Materia
VEHICLE ROUTING PROBLEM
GENETIC ALGORITHMS
MODELING
OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/59378
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A New Approach to the Optimization of the CVRP through Genetic AlgorithmsFrutos, MarianoTohmé, Fernando AbelVEHICLE ROUTING PROBLEMGENETIC ALGORITHMSMODELINGOPTIMIZATIONhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2This paper presents a new approach to the analysis of complex distribution problems under capacity constraints. These problems are known in the literature as CVRPs (Capacitated Vehicle Routing Problems). The procedure introduced in this paper optimizes a transformed variant of a CVRP. It starts generating feasible clusters and codifies their ordering. In the next stage the procedure feeds this information into a genetic algorithm for its optimization. This makes the algo-rithm independent of the constraints and improves its performance. Van Breedam problems have been used to test this technique. While the results obtained are similar to those in other works, the processing times are longer.Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; ArgentinaScientific Research2012-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59378Frutos, Mariano; Tohmé, Fernando Abel; A New Approach to the Optimization of the CVRP through Genetic Algorithms; Scientific Research; American Journal of Operations Research; 2; 4; 11-2012; 495-5012160-88302160-8849CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/journal/PaperInformation.aspx?PaperID=25145#.VUUh9_l_Okoinfo:eu-repo/semantics/altIdentifier/doi/10.4236/ajor.2012.24058info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:13:01Zoai:ri.conicet.gov.ar:11336/59378instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:13:01.294CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A New Approach to the Optimization of the CVRP through Genetic Algorithms
title A New Approach to the Optimization of the CVRP through Genetic Algorithms
spellingShingle A New Approach to the Optimization of the CVRP through Genetic Algorithms
Frutos, Mariano
VEHICLE ROUTING PROBLEM
GENETIC ALGORITHMS
MODELING
OPTIMIZATION
title_short A New Approach to the Optimization of the CVRP through Genetic Algorithms
title_full A New Approach to the Optimization of the CVRP through Genetic Algorithms
title_fullStr A New Approach to the Optimization of the CVRP through Genetic Algorithms
title_full_unstemmed A New Approach to the Optimization of the CVRP through Genetic Algorithms
title_sort A New Approach to the Optimization of the CVRP through Genetic Algorithms
dc.creator.none.fl_str_mv Frutos, Mariano
Tohmé, Fernando Abel
author Frutos, Mariano
author_facet Frutos, Mariano
Tohmé, Fernando Abel
author_role author
author2 Tohmé, Fernando Abel
author2_role author
dc.subject.none.fl_str_mv VEHICLE ROUTING PROBLEM
GENETIC ALGORITHMS
MODELING
OPTIMIZATION
topic VEHICLE ROUTING PROBLEM
GENETIC ALGORITHMS
MODELING
OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper presents a new approach to the analysis of complex distribution problems under capacity constraints. These problems are known in the literature as CVRPs (Capacitated Vehicle Routing Problems). The procedure introduced in this paper optimizes a transformed variant of a CVRP. It starts generating feasible clusters and codifies their ordering. In the next stage the procedure feeds this information into a genetic algorithm for its optimization. This makes the algo-rithm independent of the constraints and improves its performance. Van Breedam problems have been used to test this technique. While the results obtained are similar to those in other works, the processing times are longer.
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina
description This paper presents a new approach to the analysis of complex distribution problems under capacity constraints. These problems are known in the literature as CVRPs (Capacitated Vehicle Routing Problems). The procedure introduced in this paper optimizes a transformed variant of a CVRP. It starts generating feasible clusters and codifies their ordering. In the next stage the procedure feeds this information into a genetic algorithm for its optimization. This makes the algo-rithm independent of the constraints and improves its performance. Van Breedam problems have been used to test this technique. While the results obtained are similar to those in other works, the processing times are longer.
publishDate 2012
dc.date.none.fl_str_mv 2012-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/59378
Frutos, Mariano; Tohmé, Fernando Abel; A New Approach to the Optimization of the CVRP through Genetic Algorithms; Scientific Research; American Journal of Operations Research; 2; 4; 11-2012; 495-501
2160-8830
2160-8849
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59378
identifier_str_mv Frutos, Mariano; Tohmé, Fernando Abel; A New Approach to the Optimization of the CVRP through Genetic Algorithms; Scientific Research; American Journal of Operations Research; 2; 4; 11-2012; 495-501
2160-8830
2160-8849
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/journal/PaperInformation.aspx?PaperID=25145#.VUUh9_l_Oko
info:eu-repo/semantics/altIdentifier/doi/10.4236/ajor.2012.24058
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Scientific Research
publisher.none.fl_str_mv Scientific Research
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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