Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of CearÃ
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFC |
Texto Completo: | http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13744 |
Resumo: | A concern of logistics management is the correct and efficient use of the available fleet. The central focus of fleet management is determining the routes that will be used in customer service and the efficient allocation of available resources (vehicles). The correct fleet management can generate a competitive advantage. There is a problem in the Operations Research dedicated to working this type of situation, the Vehicle Routing Problem (VRP). The VRP tries to generate the most economical route to efficient use of the available fleet. The case study discussed in this work was a particular situation VRP where there is a heterogeneous fleet and where the collections and deliveries of passengers are carried at separate times. To solve this problem we designed a Genetic Algorithm. Additionally, three different crossover operators were tested in the search for better results. At the end of the study, the Genetic Algorithm was capable of solving the problem in a short time and finding the most economical way to generate routes, using efficiently the fleet and fulfilling all requests. |
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Biblioteca Digital de Teses e Dissertações da UFC |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisGenetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of CearÃAlgoritmo genÃtico para o problema de roteirizaÃÃo de veÃculos com frota heterogÃnea e coleta e entrega separadas: estudo de caso na Secretaria do Trabalho e Desenvolvimento Social do Estado do CearÃ2014-07-31Josà Lassance de Castro Silva23574445334SÃlvia Maria de Freitas32429959372http://buscatextual.cnpq.br/buscatextual/visualizacv.jsp?id=K4727554D8Michael Ferreira de Souza0907168272202795995360CÃsar Augusto Chaves e Sousa FilhoUniversidade Federal do CearÃPrograma de PÃs-GraduaÃÃo em LogÃstica e Pesquisa OperacionalUFCBRProblema de roteamento de veÃculos Algoritmo genÃtico Frota heterogÃneaVehicle Routing Problem, Heterogeneous Fleet, Pickup and Delivery Separated, Genetic Algorithm, Logistics Operational ResearchPESQUISA OPERACIONALA concern of logistics management is the correct and efficient use of the available fleet. The central focus of fleet management is determining the routes that will be used in customer service and the efficient allocation of available resources (vehicles). The correct fleet management can generate a competitive advantage. There is a problem in the Operations Research dedicated to working this type of situation, the Vehicle Routing Problem (VRP). The VRP tries to generate the most economical route to efficient use of the available fleet. The case study discussed in this work was a particular situation VRP where there is a heterogeneous fleet and where the collections and deliveries of passengers are carried at separate times. To solve this problem we designed a Genetic Algorithm. Additionally, three different crossover operators were tested in the search for better results. At the end of the study, the Genetic Algorithm was capable of solving the problem in a short time and finding the most economical way to generate routes, using efficiently the fleet and fulfilling all requests.Uma das preocupaÃÃes da gestÃo logÃstica à a correta e eficiente utilizaÃÃo da frota disponÃvel. O foco central da gestÃo da frota està em determinar as rotas que serÃo utilizadas no atendimento aos clientes e a alocaÃÃo eficiente dos recursos (veÃculos) disponÃveis. A gestÃo correta da frota pode gerar um diferencial competitivo. Existe na Pesquisa Operacional um problema dedicado a trabalhar este tipo de situaÃÃo, denominado Problema de Roteamento de VeÃculos (PRV). O PRV procura gerar a rota mais econÃmica com utilizaÃÃo eficiente da frota disponÃvel. No estudo de caso, realizado neste trabalho, foi abordada uma situaÃÃo particular do PRV onde hà uma frota heterogÃnea e as coletas e entregas de passageiros sÃo realizadas em momentos separados. Para a resoluÃÃo deste problema foi desenvolvido e implementado um Algoritmo GenÃtico (AG). Adicionalmente, trÃs operadores de cruzamento diferentes foram testados na busca dos melhores resultados encontrados pelo AG. Ao final, o Algoritmo GenÃtico conseguiu se mostrar capaz de resolver o problema em tempo hÃbil e de maneira a gerar rotas mais econÃmicas, utilizando eficientemente a frota e atendendo todas as solicitaÃÃes.http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13744application/pdfinfo:eu-repo/semantics/openAccessporreponame:Biblioteca Digital de Teses e Dissertações da UFCinstname:Universidade Federal do Cearáinstacron:UFC2019-01-21T11:27:05Zmail@mail.com - |
dc.title.en.fl_str_mv |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
dc.title.alternative.pt.fl_str_mv |
Algoritmo genÃtico para o problema de roteirizaÃÃo de veÃculos com frota heterogÃnea e coleta e entrega separadas: estudo de caso na Secretaria do Trabalho e Desenvolvimento Social do Estado do Cearà |
title |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
spellingShingle |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà CÃsar Augusto Chaves e Sousa Filho Problema de roteamento de veÃculos Algoritmo genÃtico Frota heterogÃnea Vehicle Routing Problem, Heterogeneous Fleet, Pickup and Delivery Separated, Genetic Algorithm, Logistics Operational Research PESQUISA OPERACIONAL |
title_short |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
title_full |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
title_fullStr |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
title_full_unstemmed |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
title_sort |
Genetic algorithm for vehicle routing problem with heterogeneous fleet and separate collection and delivery: a case in the Secretariat of Labor and Social Development of the State of Cearà |
author |
CÃsar Augusto Chaves e Sousa Filho |
author_facet |
CÃsar Augusto Chaves e Sousa Filho |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Josà Lassance de Castro Silva |
dc.contributor.advisor1ID.fl_str_mv |
23574445334 |
dc.contributor.referee1.fl_str_mv |
SÃlvia Maria de Freitas |
dc.contributor.referee1ID.fl_str_mv |
32429959372 |
dc.contributor.referee1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.jsp?id=K4727554D8 |
dc.contributor.referee2.fl_str_mv |
Michael Ferreira de Souza |
dc.contributor.referee2ID.fl_str_mv |
09071682722 |
dc.contributor.authorID.fl_str_mv |
02795995360 |
dc.contributor.author.fl_str_mv |
CÃsar Augusto Chaves e Sousa Filho |
contributor_str_mv |
Josà Lassance de Castro Silva SÃlvia Maria de Freitas Michael Ferreira de Souza |
dc.subject.por.fl_str_mv |
Problema de roteamento de veÃculos Algoritmo genÃtico Frota heterogÃnea |
topic |
Problema de roteamento de veÃculos Algoritmo genÃtico Frota heterogÃnea Vehicle Routing Problem, Heterogeneous Fleet, Pickup and Delivery Separated, Genetic Algorithm, Logistics Operational Research PESQUISA OPERACIONAL |
dc.subject.eng.fl_str_mv |
Vehicle Routing Problem, Heterogeneous Fleet, Pickup and Delivery Separated, Genetic Algorithm, Logistics Operational Research |
dc.subject.cnpq.fl_str_mv |
PESQUISA OPERACIONAL |
dc.description.abstract.por.fl_txt_mv |
A concern of logistics management is the correct and efficient use of the available fleet. The central focus of fleet management is determining the routes that will be used in customer service and the efficient allocation of available resources (vehicles). The correct fleet management can generate a competitive advantage. There is a problem in the Operations Research dedicated to working this type of situation, the Vehicle Routing Problem (VRP). The VRP tries to generate the most economical route to efficient use of the available fleet. The case study discussed in this work was a particular situation VRP where there is a heterogeneous fleet and where the collections and deliveries of passengers are carried at separate times. To solve this problem we designed a Genetic Algorithm. Additionally, three different crossover operators were tested in the search for better results. At the end of the study, the Genetic Algorithm was capable of solving the problem in a short time and finding the most economical way to generate routes, using efficiently the fleet and fulfilling all requests. Uma das preocupaÃÃes da gestÃo logÃstica à a correta e eficiente utilizaÃÃo da frota disponÃvel. O foco central da gestÃo da frota està em determinar as rotas que serÃo utilizadas no atendimento aos clientes e a alocaÃÃo eficiente dos recursos (veÃculos) disponÃveis. A gestÃo correta da frota pode gerar um diferencial competitivo. Existe na Pesquisa Operacional um problema dedicado a trabalhar este tipo de situaÃÃo, denominado Problema de Roteamento de VeÃculos (PRV). O PRV procura gerar a rota mais econÃmica com utilizaÃÃo eficiente da frota disponÃvel. No estudo de caso, realizado neste trabalho, foi abordada uma situaÃÃo particular do PRV onde hà uma frota heterogÃnea e as coletas e entregas de passageiros sÃo realizadas em momentos separados. Para a resoluÃÃo deste problema foi desenvolvido e implementado um Algoritmo GenÃtico (AG). Adicionalmente, trÃs operadores de cruzamento diferentes foram testados na busca dos melhores resultados encontrados pelo AG. Ao final, o Algoritmo GenÃtico conseguiu se mostrar capaz de resolver o problema em tempo hÃbil e de maneira a gerar rotas mais econÃmicas, utilizando eficientemente a frota e atendendo todas as solicitaÃÃes. |
description |
A concern of logistics management is the correct and efficient use of the available fleet. The central focus of fleet management is determining the routes that will be used in customer service and the efficient allocation of available resources (vehicles). The correct fleet management can generate a competitive advantage. There is a problem in the Operations Research dedicated to working this type of situation, the Vehicle Routing Problem (VRP). The VRP tries to generate the most economical route to efficient use of the available fleet. The case study discussed in this work was a particular situation VRP where there is a heterogeneous fleet and where the collections and deliveries of passengers are carried at separate times. To solve this problem we designed a Genetic Algorithm. Additionally, three different crossover operators were tested in the search for better results. At the end of the study, the Genetic Algorithm was capable of solving the problem in a short time and finding the most economical way to generate routes, using efficiently the fleet and fulfilling all requests. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-07-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
status_str |
publishedVersion |
format |
masterThesis |
dc.identifier.uri.fl_str_mv |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13744 |
url |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13744 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Cearà |
dc.publisher.program.fl_str_mv |
Programa de PÃs-GraduaÃÃo em LogÃstica e Pesquisa Operacional |
dc.publisher.initials.fl_str_mv |
UFC |
dc.publisher.country.fl_str_mv |
BR |
publisher.none.fl_str_mv |
Universidade Federal do Cearà |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFC instname:Universidade Federal do Ceará instacron:UFC |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFC |
collection |
Biblioteca Digital de Teses e Dissertações da UFC |
instname_str |
Universidade Federal do Ceará |
instacron_str |
UFC |
institution |
UFC |
repository.name.fl_str_mv |
-
|
repository.mail.fl_str_mv |
mail@mail.com |
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1643295201092960256 |