Attitude quantifier based possibility distribution generation method for hesitant fuzzy linguistic group decision making

被引:18
作者
Hao, Jingjing [1 ,2 ]
Chiclana, Francisco [2 ,3 ]
机构
[1] Zhejiang Normal Univ, Coll Econ & Management, Jinhua, Zhejiang, Peoples R China
[2] De Montfort Univ, Fac Technol, Inst Artificial Intelligence, Leicester, Leics, England
[3] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain
基金
中国国家自然科学基金;
关键词
Hesitant fuzzy linguistic term set; Possibility distribution; Attitude index; Feedback mechanism; Consensus reaching process; CONSENSUS MODEL; TERM SETS; AGGREGATION OPERATORS; INFORMATION; MINIMUM; COST;
D O I
10.1016/j.ins.2020.01.026
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The possibility distribution-based approach is one of the powerful tools available to manage hesitant fuzzy linguistic term set (HFLTS) information. However, existing possibility distribution studies have not considered the experts satisfied preference for HFLTSs in the process of generating the possibility distribution. This paper aims at filling this research gap. To achieve this goal, a novel possibility distribution generation method based on the concept of linguistic quantifier is proposed. This is accomplished by defining a new attitude linguistic quantifier, which is supported with theoretical results to analyze the relationship between the proposed attitude linguistic quantifier with the original linguistic quantifier, attitude indices and the expected linguistic term. The new possibility distribution generation method is proved to be (1) more general than the two main existing approaches, which are particular cases for specific linguistic quantifiers; and (2) useful to implement the concept of soft majority in the resolution process of the decision making situation. Additionally, a new two stages feedback mechanism of attitude adjustment and assessment adjustment is devised to guarantee the convergence of the consensus reaching process. Finally, a framework of group decision making with HFLTSs information is presented and an illustrative example is conducted to verify the proposed method. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:341 / 360
页数:20
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