An extension of the ELECTRE approach with multi-valued neutrosophic information
In this paper, an extension Elimination and Choice Translating Reality (ELECTRE) method is introduced to handle multi-valued neutrosophic multi-criteria decision-making (MCDM) problems. First of all, some outranking relations for multi-valued neutrosophic numbers (MVNNs), which are based on traditio...
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| Veröffentlicht in: | Neural computing & applications 2017-12, Vol.28 (Suppl 1), p.1011-1022 |
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| container_title | Neural computing & applications |
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| creator | Peng, Juan-juan Wang, Jian-qiang Wu, Xiao-hui |
| description | In this paper, an extension Elimination and Choice Translating Reality (ELECTRE) method is introduced to handle multi-valued neutrosophic multi-criteria decision-making (MCDM) problems. First of all, some outranking relations for multi-valued neutrosophic numbers (MVNNs), which are based on traditional ELECTRE methods, are defined, and several properties are analyzed. In the next place, an outranking method to deal with MCDM problems similar to ELECTRE III, where weights and data are in the form of MVNNs, is developed. At last, an example is provided to demonstrate the proposed approach and testify its validity and feasibility. This study is supported by the comparison analysis with other existing methods. |
| doi_str_mv | 10.1007/s00521-016-2411-8 |
| format | Article |
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First of all, some outranking relations for multi-valued neutrosophic numbers (MVNNs), which are based on traditional ELECTRE methods, are defined, and several properties are analyzed. In the next place, an outranking method to deal with MCDM problems similar to ELECTRE III, where weights and data are in the form of MVNNs, is developed. At last, an example is provided to demonstrate the proposed approach and testify its validity and feasibility. This study is supported by the comparison analysis with other existing methods.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-016-2411-8</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Decision making ; Feasibility studies ; Image Processing and Computer Vision ; Original Article ; Probability and Statistics in Computer Science</subject><ispartof>Neural computing & applications, 2017-12, Vol.28 (Suppl 1), p.1011-1022</ispartof><rights>The Natural Computing Applications Forum 2016</rights><rights>Copyright Springer Science & Business Media 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-c0bf089815bfd7b0f19ac1ee1d429b0ed6e3ad76904c2e9a7331dbc34bad7a2c3</citedby><cites>FETCH-LOGICAL-c316t-c0bf089815bfd7b0f19ac1ee1d429b0ed6e3ad76904c2e9a7331dbc34bad7a2c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-016-2411-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-016-2411-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Peng, Juan-juan</creatorcontrib><creatorcontrib>Wang, Jian-qiang</creatorcontrib><creatorcontrib>Wu, Xiao-hui</creatorcontrib><title>An extension of the ELECTRE approach with multi-valued neutrosophic information</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>In this paper, an extension Elimination and Choice Translating Reality (ELECTRE) method is introduced to handle multi-valued neutrosophic multi-criteria decision-making (MCDM) problems. First of all, some outranking relations for multi-valued neutrosophic numbers (MVNNs), which are based on traditional ELECTRE methods, are defined, and several properties are analyzed. In the next place, an outranking method to deal with MCDM problems similar to ELECTRE III, where weights and data are in the form of MVNNs, is developed. At last, an example is provided to demonstrate the proposed approach and testify its validity and feasibility. 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| subjects | Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Decision making Feasibility studies Image Processing and Computer Vision Original Article Probability and Statistics in Computer Science |
| title | An extension of the ELECTRE approach with multi-valued neutrosophic information |
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