Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method

New energy vehicles can improve the environmental pollution and thus benefit people’s healthy life. As a core component of new energy vehicles, batteries play a crucial role in the performance of new energy vehicles. There are many factors to be considered when selecting the battery for a new energy...

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Autores principales:	Huchang Liao, Zhihang Liu, Audrius Banaitis, Edmundas Kazimieras Zavadskas, Xiang Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Vilnius Gediminas Technical University 2021
Materias:	new energy vehicle battery supplier development best-worst method probabilistic linguistic term set utastar Transportation engineering TA1001-1280
Acceso en línea:	https://doaj.org/article/9b35e64e2a4040e39d312373db2ca695
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spelling	oai:doaj.org-article:9b35e64e2a4040e39d312373db2ca6952021-11-25T13:02:47ZBattery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method1648-41421648-348010.3846/transport.2021.14710https://doaj.org/article/9b35e64e2a4040e39d312373db2ca6952021-05-01T00:00:00Zhttps://journals.vgtu.lt/index.php/Transport/article/view/14710https://doaj.org/toc/1648-4142https://doaj.org/toc/1648-3480New energy vehicles can improve the environmental pollution and thus benefit people’s healthy life. As a core component of new energy vehicles, batteries play a crucial role in the performance of new energy vehicles. There are many factors to be considered when selecting the battery for a new energy vehicle, so it can be regarded as a MCDM problem. This study builds a useful model by combining the PLTS with the UTASTAR method. Firstly, to represent the uncertain and fuzzy information of experts, we use the PLTSs to accurately express the linguistic information of experts. Given that the weights of criteria are often different and there are some preferences for criteria among experts, we use the BWM to determine the weights of criteria, which can deal with hesitant information and make the result suitable for experts’ preferences. The method proposed in this study can sort all alternatives based on a small amount of data. To show its applicability, we implement the method in the selection of new energy vehicle battery suppliers. Comparative analysis and discussions are made to verify the effectiveness of the method. First published online 10 May 2021Huchang LiaoZhihang LiuAudrius BanaitisEdmundas Kazimieras ZavadskasXiang ZhouVilnius Gediminas Technical Universityarticlenew energy vehiclebattery supplier developmentbest-worst methodprobabilistic linguistic term setutastarTransportation engineeringTA1001-1280ENTransport, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	new energy vehicle battery supplier development best-worst method probabilistic linguistic term set utastar Transportation engineering TA1001-1280
spellingShingle	new energy vehicle battery supplier development best-worst method probabilistic linguistic term set utastar Transportation engineering TA1001-1280 Huchang Liao Zhihang Liu Audrius Banaitis Edmundas Kazimieras Zavadskas Xiang Zhou Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
description	New energy vehicles can improve the environmental pollution and thus benefit people’s healthy life. As a core component of new energy vehicles, batteries play a crucial role in the performance of new energy vehicles. There are many factors to be considered when selecting the battery for a new energy vehicle, so it can be regarded as a MCDM problem. This study builds a useful model by combining the PLTS with the UTASTAR method. Firstly, to represent the uncertain and fuzzy information of experts, we use the PLTSs to accurately express the linguistic information of experts. Given that the weights of criteria are often different and there are some preferences for criteria among experts, we use the BWM to determine the weights of criteria, which can deal with hesitant information and make the result suitable for experts’ preferences. The method proposed in this study can sort all alternatives based on a small amount of data. To show its applicability, we implement the method in the selection of new energy vehicle battery suppliers. Comparative analysis and discussions are made to verify the effectiveness of the method. First published online 10 May 2021
format	article
author	Huchang Liao Zhihang Liu Audrius Banaitis Edmundas Kazimieras Zavadskas Xiang Zhou
author_facet	Huchang Liao Zhihang Liu Audrius Banaitis Edmundas Kazimieras Zavadskas Xiang Zhou
author_sort	Huchang Liao
title	Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
title_short	Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
title_full	Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
title_fullStr	Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
title_full_unstemmed	Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method
title_sort	battery supplier development for new energy vehicles by a probabilistic linguistic utastar method
publisher	Vilnius Gediminas Technical University
publishDate	2021
url	https://doaj.org/article/9b35e64e2a4040e39d312373db2ca695
work_keys_str_mv	AT huchangliao batterysupplierdevelopmentfornewenergyvehiclesbyaprobabilisticlinguisticutastarmethod AT zhihangliu batterysupplierdevelopmentfornewenergyvehiclesbyaprobabilisticlinguisticutastarmethod AT audriusbanaitis batterysupplierdevelopmentfornewenergyvehiclesbyaprobabilisticlinguisticutastarmethod AT edmundaskazimieraszavadskas batterysupplierdevelopmentfornewenergyvehiclesbyaprobabilisticlinguisticutastarmethod AT xiangzhou batterysupplierdevelopmentfornewenergyvehiclesbyaprobabilisticlinguisticutastarmethod
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Battery supplier development for new energy vehicles by a probabilistic linguistic UTASTAR method

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