Multicriteria Methodology for Selection of a Personal Electric Vehicle
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/1415 |
Resumo: | A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process. |
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Brazilian Journal of Operations & Production Management (Online) |
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Multicriteria Methodology for Selection of a Personal Electric VehicleMulticriteriaPROMETHEE AHP Electric vehiclesA methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2023-04-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch paperapplication/pdfhttps://bjopm.org.br/bjopm/article/view/141510.14488/BJOPM.1415.2023Brazilian Journal of Operations & Production Management; Vol. 20 No. 2 (2023); 1415 2237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/1415/1042Copyright (c) 2023 Carlos Francisco Simões Gomes, Francisco Sousa, Teresa Pereira, Marisa Oliveira, Luís Norberto Miranda Torreshttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessGomes, Carlos Francisco SimõesSousa, FranciscoPereira, TeresaOliveira, MarisaTorres, Luís Norberto Miranda2023-04-04T13:19:52Zoai:ojs.bjopm.org.br:article/1415Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-04-04T13:19:52Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
title |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
spellingShingle |
Multicriteria Methodology for Selection of a Personal Electric Vehicle Gomes, Carlos Francisco Simões Multicriteria PROMETHEE AHP Electric vehicles |
title_short |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
title_full |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
title_fullStr |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
title_full_unstemmed |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
title_sort |
Multicriteria Methodology for Selection of a Personal Electric Vehicle |
author |
Gomes, Carlos Francisco Simões |
author_facet |
Gomes, Carlos Francisco Simões Sousa, Francisco Pereira, Teresa Oliveira, Marisa Torres, Luís Norberto Miranda |
author_role |
author |
author2 |
Sousa, Francisco Pereira, Teresa Oliveira, Marisa Torres, Luís Norberto Miranda |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Gomes, Carlos Francisco Simões Sousa, Francisco Pereira, Teresa Oliveira, Marisa Torres, Luís Norberto Miranda |
dc.subject.por.fl_str_mv |
Multicriteria PROMETHEE AHP Electric vehicles |
topic |
Multicriteria PROMETHEE AHP Electric vehicles |
description |
A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-04 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Research paper |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1415 10.14488/BJOPM.1415.2023 |
url |
https://bjopm.org.br/bjopm/article/view/1415 |
identifier_str_mv |
10.14488/BJOPM.1415.2023 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/1415/1042 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 20 No. 2 (2023); 1415 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051459445981184 |