Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment
Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expr...
Ausführliche Beschreibung
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| Autor*in: |
Alghamdi, M. A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 20(2018), 6 vom: 21. Mai, Seite 2057-2064 |
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| Übergeordnetes Werk: |
volume:20 ; year:2018 ; number:6 ; day:21 ; month:05 ; pages:2057-2064 |
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| DOI / URN: |
10.1007/s40815-018-0499-y |
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SPR037859196 |
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| 520 | |a Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. | ||
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| 700 | 1 | |a Alshehri, Noura Omair |4 aut | |
| 700 | 1 | |a Akram, Muhammad |4 aut | |
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10.1007/s40815-018-0499-y doi (DE-627)SPR037859196 (SPR)s40815-018-0499-y-e DE-627 ger DE-627 rakwb eng Alghamdi, M. A. verfasserin aut Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. Bipolar fuzzy set (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 ELECTRE I method (dpeaa)DE-He213 Alshehri, Noura Omair aut Akram, Muhammad aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2018), 6 vom: 21. Mai, Seite 2057-2064 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2018 number:6 day:21 month:05 pages:2057-2064 https://dx.doi.org/10.1007/s40815-018-0499-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2018 6 21 05 2057-2064 |
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10.1007/s40815-018-0499-y doi (DE-627)SPR037859196 (SPR)s40815-018-0499-y-e DE-627 ger DE-627 rakwb eng Alghamdi, M. A. verfasserin aut Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. Bipolar fuzzy set (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 ELECTRE I method (dpeaa)DE-He213 Alshehri, Noura Omair aut Akram, Muhammad aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2018), 6 vom: 21. Mai, Seite 2057-2064 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2018 number:6 day:21 month:05 pages:2057-2064 https://dx.doi.org/10.1007/s40815-018-0499-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2018 6 21 05 2057-2064 |
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10.1007/s40815-018-0499-y doi (DE-627)SPR037859196 (SPR)s40815-018-0499-y-e DE-627 ger DE-627 rakwb eng Alghamdi, M. A. verfasserin aut Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. Bipolar fuzzy set (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 ELECTRE I method (dpeaa)DE-He213 Alshehri, Noura Omair aut Akram, Muhammad aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2018), 6 vom: 21. Mai, Seite 2057-2064 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2018 number:6 day:21 month:05 pages:2057-2064 https://dx.doi.org/10.1007/s40815-018-0499-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2018 6 21 05 2057-2064 |
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10.1007/s40815-018-0499-y doi (DE-627)SPR037859196 (SPR)s40815-018-0499-y-e DE-627 ger DE-627 rakwb eng Alghamdi, M. A. verfasserin aut Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. Bipolar fuzzy set (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 ELECTRE I method (dpeaa)DE-He213 Alshehri, Noura Omair aut Akram, Muhammad aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2018), 6 vom: 21. Mai, Seite 2057-2064 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2018 number:6 day:21 month:05 pages:2057-2064 https://dx.doi.org/10.1007/s40815-018-0499-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2018 6 21 05 2057-2064 |
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Alghamdi, M. A. |
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Alghamdi, M. A. misc Bipolar fuzzy set misc TOPSIS method misc ELECTRE I method Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment |
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Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment Bipolar fuzzy set (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 ELECTRE I method (dpeaa)DE-He213 |
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Alghamdi, M. A. Alshehri, Noura Omair Akram, Muhammad |
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Alghamdi, M. A. |
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multi-criteria decision-making methods in bipolar fuzzy environment |
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Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment |
| abstract |
Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstractGer |
Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment |
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Alshehri, Noura Omair Akram, Muhammad |
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10.1007/s40815-018-0499-y |
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2024-07-03T14:46:35.993Z |
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A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. 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