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A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling
•Propose the concept of linguistic Pythagorean hesitant fuzzy set (LPHFS).•The weight determining models for expert panel and criteria set under the LPHF environment are derived, respectively.•Put forward the linguistic Pythagorean hesitant fuzzy MULTIMOORA method and construct a multi-criteria deci...
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Published in: | Expert systems with applications 2022-07, Vol.198, p.116808, Article 116808 |
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description | •Propose the concept of linguistic Pythagorean hesitant fuzzy set (LPHFS).•The weight determining models for expert panel and criteria set under the LPHF environment are derived, respectively.•Put forward the linguistic Pythagorean hesitant fuzzy MULTIMOORA method and construct a multi-criteria decision-making framework.•A practical case of 3PRL provider selection for electric vehicle power battery recycling in China is given.
Electric vehicle power battery recycling (EVPBR) is an effective way to utilize resources and reduce environmental damage. In order to ensure the safety and efficiency of electric vehicle batteries (EVBs) reuse and remanufacture, many EVB manufacturers are seeking cooperation with third-party reverse logistics (3PRL) providers to conduct the pre-treatment and transportation of post-used batteries. However, the selection of 3PRL provider is a matter of complex multi-criteria decision-making (MCDM) affected by numerous associated factors. The goal of this paper is to present a linguistic Pythagorean hesitant fuzzy MULTIMOORA method to investigate the selection of 3PRL providers for EVPBR. Firstly, given that the complexity of evaluation information, the linguistic Pythagorean hesitant fuzzy set (LPHFS) is defined in detail. On the basis of new evaluation representation tool, the weight determining models for expert panel and criteria set based on correlation consensus degrees and maximum deviation are derived, respectively. Then a MULTIMOORA method under the linguistic Pythagorean hesitant fuzzy environment is constructed. Later, the evaluation criteria system for the provider capability analysis is set up, and the proposed MCDM approach is applied to select the most suitable 3PRL provider for EVPBR in China. Finally, the sensitivity and comparative analysis results justify the robustness and feasibility of the proposed method. |
doi_str_mv | 10.1016/j.eswa.2022.116808 |
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Electric vehicle power battery recycling (EVPBR) is an effective way to utilize resources and reduce environmental damage. In order to ensure the safety and efficiency of electric vehicle batteries (EVBs) reuse and remanufacture, many EVB manufacturers are seeking cooperation with third-party reverse logistics (3PRL) providers to conduct the pre-treatment and transportation of post-used batteries. However, the selection of 3PRL provider is a matter of complex multi-criteria decision-making (MCDM) affected by numerous associated factors. The goal of this paper is to present a linguistic Pythagorean hesitant fuzzy MULTIMOORA method to investigate the selection of 3PRL providers for EVPBR. Firstly, given that the complexity of evaluation information, the linguistic Pythagorean hesitant fuzzy set (LPHFS) is defined in detail. On the basis of new evaluation representation tool, the weight determining models for expert panel and criteria set based on correlation consensus degrees and maximum deviation are derived, respectively. Then a MULTIMOORA method under the linguistic Pythagorean hesitant fuzzy environment is constructed. Later, the evaluation criteria system for the provider capability analysis is set up, and the proposed MCDM approach is applied to select the most suitable 3PRL provider for EVPBR in China. Finally, the sensitivity and comparative analysis results justify the robustness and feasibility of the proposed method.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2022.116808</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Complexity ; Decision making ; Electric vehicle power battery recycling (EVPBR) ; Electric vehicles ; Fuzzy logic ; Fuzzy sets ; Linguistic Pythagorean hesitant fuzzy set (LPHFS) ; Linguistics ; Multi-criteria decision-making (MCDM) ; MULTIMOORA method ; Multiple criterion ; Recycling ; Remanufacturing ; Reverse logistics ; Third-party reverse logistics (3PRL) providers</subject><ispartof>Expert systems with applications, 2022-07, Vol.198, p.116808, Article 116808</ispartof><rights>2022 Elsevier Ltd</rights><rights>Copyright Elsevier BV Jul 15, 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-6c7916b82f09df7b9a3e5648e57fe35b4934c695c2aff7876fe0b2af8a6e50933</citedby><cites>FETCH-LOGICAL-c328t-6c7916b82f09df7b9a3e5648e57fe35b4934c695c2aff7876fe0b2af8a6e50933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925,31269</link.rule.ids></links><search><creatorcontrib>Yang, Chengxiu</creatorcontrib><creatorcontrib>Wang, Qianzhe</creatorcontrib><creatorcontrib>Pan, Mengchun</creatorcontrib><creatorcontrib>Hu, Jiafei</creatorcontrib><creatorcontrib>Peng, Weidong</creatorcontrib><creatorcontrib>Zhang, Jiaqiang</creatorcontrib><creatorcontrib>Zhang, Liang</creatorcontrib><title>A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling</title><title>Expert systems with applications</title><description>•Propose the concept of linguistic Pythagorean hesitant fuzzy set (LPHFS).•The weight determining models for expert panel and criteria set under the LPHF environment are derived, respectively.•Put forward the linguistic Pythagorean hesitant fuzzy MULTIMOORA method and construct a multi-criteria decision-making framework.•A practical case of 3PRL provider selection for electric vehicle power battery recycling in China is given.
Electric vehicle power battery recycling (EVPBR) is an effective way to utilize resources and reduce environmental damage. In order to ensure the safety and efficiency of electric vehicle batteries (EVBs) reuse and remanufacture, many EVB manufacturers are seeking cooperation with third-party reverse logistics (3PRL) providers to conduct the pre-treatment and transportation of post-used batteries. However, the selection of 3PRL provider is a matter of complex multi-criteria decision-making (MCDM) affected by numerous associated factors. The goal of this paper is to present a linguistic Pythagorean hesitant fuzzy MULTIMOORA method to investigate the selection of 3PRL providers for EVPBR. Firstly, given that the complexity of evaluation information, the linguistic Pythagorean hesitant fuzzy set (LPHFS) is defined in detail. On the basis of new evaluation representation tool, the weight determining models for expert panel and criteria set based on correlation consensus degrees and maximum deviation are derived, respectively. Then a MULTIMOORA method under the linguistic Pythagorean hesitant fuzzy environment is constructed. Later, the evaluation criteria system for the provider capability analysis is set up, and the proposed MCDM approach is applied to select the most suitable 3PRL provider for EVPBR in China. Finally, the sensitivity and comparative analysis results justify the robustness and feasibility of the proposed method.</description><subject>Complexity</subject><subject>Decision making</subject><subject>Electric vehicle power battery recycling (EVPBR)</subject><subject>Electric vehicles</subject><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Linguistic Pythagorean hesitant fuzzy set (LPHFS)</subject><subject>Linguistics</subject><subject>Multi-criteria decision-making (MCDM)</subject><subject>MULTIMOORA method</subject><subject>Multiple criterion</subject><subject>Recycling</subject><subject>Remanufacturing</subject><subject>Reverse logistics</subject><subject>Third-party reverse logistics (3PRL) providers</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>7T9</sourceid><recordid>eNp9UctO3DAUtSqQOlB-oCtLXWfwI7ETqZsRghZp0FQVrC3HuZ54FOKp7RkUPqVfi8OwZnXP4ryuDkLfKVlSQsX1bgnxRS8ZYWxJqahJ_QUtaC15IWTDz9CCNJUsSirLr-gixh0hVBIiF-j_Cg9u3B5cTM7gP1Pq9dYH0CPuIbqkx4Tt4fV1wg9P68f7h83m7wo_Q-p9h60POPUudMVehzThAEcIEfDgt-9uEe-DP7oOAo4wgEnOj9hb_I5DTjtC78wAeO9fMqfVKUGYbcxk5k7f0LnVQ4Srj3uJnu5uH29-F-vNr_ub1bownNWpEEY2VLQ1s6TprGwbzaESZQ2VtMCrtmx4aURTGaatlbUUFkibca0FVKTh_BL9OPnmuv8OEJPa-UMYc6RiQnIuhWAys9iJZYKPMYBV--CedZgUJWreQO3UvIGaN1CnDbLo50kEuf_RQVDROBgNdC6_mVTn3WfyN1Kok5o</recordid><startdate>20220715</startdate><enddate>20220715</enddate><creator>Yang, Chengxiu</creator><creator>Wang, Qianzhe</creator><creator>Pan, Mengchun</creator><creator>Hu, Jiafei</creator><creator>Peng, Weidong</creator><creator>Zhang, Jiaqiang</creator><creator>Zhang, Liang</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7T9</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20220715</creationdate><title>A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling</title><author>Yang, Chengxiu ; Wang, Qianzhe ; Pan, Mengchun ; Hu, Jiafei ; Peng, Weidong ; Zhang, Jiaqiang ; Zhang, Liang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-6c7916b82f09df7b9a3e5648e57fe35b4934c695c2aff7876fe0b2af8a6e50933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Complexity</topic><topic>Decision making</topic><topic>Electric vehicle power battery recycling (EVPBR)</topic><topic>Electric vehicles</topic><topic>Fuzzy logic</topic><topic>Fuzzy sets</topic><topic>Linguistic Pythagorean hesitant fuzzy set (LPHFS)</topic><topic>Linguistics</topic><topic>Multi-criteria decision-making (MCDM)</topic><topic>MULTIMOORA method</topic><topic>Multiple criterion</topic><topic>Recycling</topic><topic>Remanufacturing</topic><topic>Reverse logistics</topic><topic>Third-party reverse logistics (3PRL) providers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Chengxiu</creatorcontrib><creatorcontrib>Wang, Qianzhe</creatorcontrib><creatorcontrib>Pan, Mengchun</creatorcontrib><creatorcontrib>Hu, Jiafei</creatorcontrib><creatorcontrib>Peng, Weidong</creatorcontrib><creatorcontrib>Zhang, Jiaqiang</creatorcontrib><creatorcontrib>Zhang, Liang</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Linguistics and Language Behavior Abstracts (LLBA)</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Chengxiu</au><au>Wang, Qianzhe</au><au>Pan, Mengchun</au><au>Hu, Jiafei</au><au>Peng, Weidong</au><au>Zhang, Jiaqiang</au><au>Zhang, Liang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling</atitle><jtitle>Expert systems with applications</jtitle><date>2022-07-15</date><risdate>2022</risdate><volume>198</volume><spage>116808</spage><pages>116808-</pages><artnum>116808</artnum><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•Propose the concept of linguistic Pythagorean hesitant fuzzy set (LPHFS).•The weight determining models for expert panel and criteria set under the LPHF environment are derived, respectively.•Put forward the linguistic Pythagorean hesitant fuzzy MULTIMOORA method and construct a multi-criteria decision-making framework.•A practical case of 3PRL provider selection for electric vehicle power battery recycling in China is given.
Electric vehicle power battery recycling (EVPBR) is an effective way to utilize resources and reduce environmental damage. In order to ensure the safety and efficiency of electric vehicle batteries (EVBs) reuse and remanufacture, many EVB manufacturers are seeking cooperation with third-party reverse logistics (3PRL) providers to conduct the pre-treatment and transportation of post-used batteries. However, the selection of 3PRL provider is a matter of complex multi-criteria decision-making (MCDM) affected by numerous associated factors. The goal of this paper is to present a linguistic Pythagorean hesitant fuzzy MULTIMOORA method to investigate the selection of 3PRL providers for EVPBR. Firstly, given that the complexity of evaluation information, the linguistic Pythagorean hesitant fuzzy set (LPHFS) is defined in detail. On the basis of new evaluation representation tool, the weight determining models for expert panel and criteria set based on correlation consensus degrees and maximum deviation are derived, respectively. Then a MULTIMOORA method under the linguistic Pythagorean hesitant fuzzy environment is constructed. Later, the evaluation criteria system for the provider capability analysis is set up, and the proposed MCDM approach is applied to select the most suitable 3PRL provider for EVPBR in China. Finally, the sensitivity and comparative analysis results justify the robustness and feasibility of the proposed method.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2022.116808</doi></addata></record> |
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subjects | Complexity Decision making Electric vehicle power battery recycling (EVPBR) Electric vehicles Fuzzy logic Fuzzy sets Linguistic Pythagorean hesitant fuzzy set (LPHFS) Linguistics Multi-criteria decision-making (MCDM) MULTIMOORA method Multiple criterion Recycling Remanufacturing Reverse logistics Third-party reverse logistics (3PRL) providers |
title | A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling |
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