[1]SHEN Qing,JIANG Yunliang,ZHANG Xiongtao.A hesitant fuzzy multi-attribute decision-making method with unknown attribute weights[J].CAAI Transactions on Intelligent Systems,2022,17(4):728-736.[doi:10.11992/tis.202107038]
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A hesitant fuzzy multi-attribute decision-making method with unknown attribute weights

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 728-736 Column: 学术论文—自然语言处理与理解 Public date: 2022-07-05
Title:
A hesitant fuzzy multi-attribute decision-making method with unknown attribute weights
Author(s):
SHEN Qing1; 3;  JIANG Yunliang2; 3;  ZHANG Xiongtao2; 3
1. School of Science and Engineering, Huzhou College, Huzhou 313000, China;
2. School of Information Engineering, Huzhou Normal University, Huzhou 313000, China;
3. Zhejiang Provincial Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou Normal University, Huzhou 313000, China
Keywords:
hesitant fuzzy multi-attribute decision making; hesitant fuzzy element; attribute weight; ternary connection number; potential function; indicative coefficient of hesitation intensity; maximum deviation; conditional decision making
CLC:
TP311
DOI:
10.11992/tis.202107038
Abstract:
For hesitant fuzzy multi-attribute decision making (HFMADM) with unknown attribute weight, using the certainty of the hesitation fuzzy element boundary and the hesitation of the membership degree within the boundary, the hesitant fuzzy element is transformed into the ternary connection number (TCN) in set pair analysis. The attribute weight is then determined using the potential function of the TCN and the principle of maximum deviation of attributes. Furthermore, a hesitant fuzzy multi-attribute decision-making model based on TCN is established under the condition of unknown attribute weight. The possible ranking of alternatives is discussed by using different hesitant intensity values in the model. The results of case calculation and comparative analysis show that the new model not only contains the findings of other models but also provides other possible ranking alternatives. The conditional decision formed by this model reflects the essential attribute of uncertainty in HFMADM, which is consistent with its practical application.
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