A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/en15072372 http://hdl.handle.net/11449/223747 |
Resumo: | Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios. |
id |
UNSP_94f921cee7ae43df0202b3de66f2b60e |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/223747 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windowscharging stationselectric vehiclesk-means algorithmlocation routing problem with time windowsmixed-integer linear programmingvehicle routingTransportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Electrical Engineering São Paulo State University (UNESP), São PauloDepartment of Industrial Engineering Los Andes UniversityDepartment of Energy Engineering São Paulo State University (UNESP), São PauloDepartment of Mathematical Sciences EAFIT UniversityDepartment of Electrical Engineering São Paulo State University (UNESP), São PauloDepartment of Energy Engineering São Paulo State University (UNESP), São PauloCAPES: 001CNPq: 152002/2016-2FAPESP: 2017/02831-8CNPq: 313047/2017-0CAPES: 88881.134450/2016-01Universidade Estadual Paulista (UNESP)Los Andes UniversityEAFIT UniversitySánchez, Danny García [UNESP]Tabares, AlejandraFaria, Lucas Teles [UNESP]Rivera, Juan CarlosFranco, John Fredy [UNESP]2022-04-28T19:52:51Z2022-04-28T19:52:51Z2022-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/en15072372Energies, v. 15, n. 7, 2022.1996-1073http://hdl.handle.net/11449/22374710.3390/en150723722-s2.0-85127417464Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnergiesinfo:eu-repo/semantics/openAccess2022-04-28T19:52:51Zoai:repositorio.unesp.br:11449/223747Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:52:51Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
title |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
spellingShingle |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows Sánchez, Danny García [UNESP] charging stations electric vehicles k-means algorithm location routing problem with time windows mixed-integer linear programming vehicle routing |
title_short |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
title_full |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
title_fullStr |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
title_full_unstemmed |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
title_sort |
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows |
author |
Sánchez, Danny García [UNESP] |
author_facet |
Sánchez, Danny García [UNESP] Tabares, Alejandra Faria, Lucas Teles [UNESP] Rivera, Juan Carlos Franco, John Fredy [UNESP] |
author_role |
author |
author2 |
Tabares, Alejandra Faria, Lucas Teles [UNESP] Rivera, Juan Carlos Franco, John Fredy [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Los Andes University EAFIT University |
dc.contributor.author.fl_str_mv |
Sánchez, Danny García [UNESP] Tabares, Alejandra Faria, Lucas Teles [UNESP] Rivera, Juan Carlos Franco, John Fredy [UNESP] |
dc.subject.por.fl_str_mv |
charging stations electric vehicles k-means algorithm location routing problem with time windows mixed-integer linear programming vehicle routing |
topic |
charging stations electric vehicles k-means algorithm location routing problem with time windows mixed-integer linear programming vehicle routing |
description |
Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:52:51Z 2022-04-28T19:52:51Z 2022-04-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/en15072372 Energies, v. 15, n. 7, 2022. 1996-1073 http://hdl.handle.net/11449/223747 10.3390/en15072372 2-s2.0-85127417464 |
url |
http://dx.doi.org/10.3390/en15072372 http://hdl.handle.net/11449/223747 |
identifier_str_mv |
Energies, v. 15, n. 7, 2022. 1996-1073 10.3390/en15072372 2-s2.0-85127417464 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Energies |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803047278475214848 |