Multi-attribute group decision making algorithm based on (p, q)-rung interval-valued orthopair fuzzy set and weight optimization model
With the aim of addressing the complexity of decision environments, uncertainty of decision information and weight determination of mutual influence between decision makers, a ( p , q )-rung interval-valued orthopair fuzzy multi-attribute group decision making algorithm based on weight optimization...
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| Published in | AIMS mathematics Vol. 8; no. 10; pp. 23997 - 24024 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
2023
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| Online Access | Get full text |
| ISSN | 2473-6988 2473-6988 |
| DOI | 10.3934/math.20231224 |
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| Abstract | With the aim of addressing the complexity of decision environments, uncertainty of decision information and weight determination of mutual influence between decision makers, a ( p , q )-rung interval-valued orthopair fuzzy multi-attribute group decision making algorithm based on weight optimization is proposed. First, in order to improve the ability of decision makers to capture their judgment in a wider space, the concept of a ( p , q )-rung interval-valued orthopair fuzzy set is proposed, and its related definition and properties are studied. Second, considering the mutual influence between decision makers and the relationship between attributes, the analytic network process (ANP) and entropy method are employed to determine the subjective and objective weights, respectively. Considering the influence of subjective and objective weights on the combination weights, the deviation degree and dispersion degree of the subjective and objective weights are taken as objective functions, and the optimal solution of the combination weights is iteratively solved by genetic algorithm. Then, based on the ( p , q )-rung interval-valued orthopair fuzzy set and weight optimization model, an improved ( p , q )-rung interval-valued orthopair fuzzy ELECTRE method is proposed. Finally, in order to verify the accuracy and robustness of the algorithm, the algorithm is applied to the example analysis of investment enterprise evaluation, and the results demonstrate that the algorithm has definite theoretical and application value. |
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| AbstractList | With the aim of addressing the complexity of decision environments, uncertainty of decision information and weight determination of mutual influence between decision makers, a ( p , q )-rung interval-valued orthopair fuzzy multi-attribute group decision making algorithm based on weight optimization is proposed. First, in order to improve the ability of decision makers to capture their judgment in a wider space, the concept of a ( p , q )-rung interval-valued orthopair fuzzy set is proposed, and its related definition and properties are studied. Second, considering the mutual influence between decision makers and the relationship between attributes, the analytic network process (ANP) and entropy method are employed to determine the subjective and objective weights, respectively. Considering the influence of subjective and objective weights on the combination weights, the deviation degree and dispersion degree of the subjective and objective weights are taken as objective functions, and the optimal solution of the combination weights is iteratively solved by genetic algorithm. Then, based on the ( p , q )-rung interval-valued orthopair fuzzy set and weight optimization model, an improved ( p , q )-rung interval-valued orthopair fuzzy ELECTRE method is proposed. Finally, in order to verify the accuracy and robustness of the algorithm, the algorithm is applied to the example analysis of investment enterprise evaluation, and the results demonstrate that the algorithm has definite theoretical and application value. |
| Author | Kong, Xiangzhi Wang, Mengmeng |
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| Cites_doi | 10.1016/j.eswa.2019.02.019 10.1007/s40815-022-01449-y 10.3233/JIFS-201429 10.1016/j.asoc.2021.107168 10.1016/j.omega.2014.11.009 10.1016/j.ins.2021.04.079 10.3934/math.2023913 10.3390/e21040364 10.3233/JIFS-169806 10.31181/dmame060129022023j 10.3390/sym15010127 10.3390/e22040442 10.1051/mfreview/2022019 10.3934/math.2023539 10.1016/j.eswa.2014.11.057 10.1109/TFUZZ.2016.2604005 10.1111/exsy.13272 10.3934/math.2023956 10.1007/s10479-005-2448-z 10.31181/dmame1902102z 10.3390/sym10090393 10.1002/(SICI)1099-1360(199711)6:6<309::AID-MCDA163>3.0.CO;2-2 10.3846/20294913.2015.1072751 10.12738/estp.2018.5.047 10.3390/sym11010056 10.1109/TFUZZ.2013.2278989 10.28924/2291-8639-18-2020-989 10.1002/int.21584 10.1007/s12652-019-01377-0 10.3390/math9182337 10.1016/j.scitotenv.2021.147763 10.1002/j.1538-7305.1948.tb01338.x 10.1016/j.dt.2019.06.019 10.1016/j.cie.2019.106231 10.14429/dsj.71.15738 10.1111/exsy.12609 10.35378/gujs.978997 10.1002/int.22163 |
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