Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets

被引:123
|
作者
Liao, Huchang C. [1 ]
Yang, Luanyi Y. [1 ]
Xu, Zeshui S. [1 ,2 ]
机构
[1] Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Hesitant fuzzy linguistic term set; Multi-criteria decision making; Concordance sets; Discordance sets; Score-deviation-based ELECTRE II method; Positive-negative ideal hesitant fuzzy linguistic elements based ELECTRE II method; PREFERENCE RELATIONS; ENVIRONMENT; INFORMATION; MODEL;
D O I
10.1016/j.asoc.2017.11.049
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In real life, it is not always the case that experts are able to assess alternatives by precise values. With the incomplete and vague information obtained, the experts are inclined to provide their assessments by eliciting linguistic terms. Considering that the experts may hesitate between several linguistic terms during the evaluating process, a new tool called the hesitant fuzzy linguistic term set (HFLTS) was introduced to represent this situation. This tool allows the experts to use several possible linguistic terms rather than a single one to appraise the performances of alternatives. In this paper, we investigate the ELECTRE II method in the HFLTS environment and propose two new approaches named the score-deviation-based ELECTRE II method and the positive and negative ideal hesitant fuzzy linguistic elements based ELECTRE II method. To fully explore the underlying preference information and specify the outranking degrees of alternatives, we set up three levels of concordance and discordance sets, which are finally devoted to ranking the alternatives more convincingly. An illustrative example is operated to show the practicability of the proposed methods and then a sensitivity analysis is performed as well to test the robustness of the two methods. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:223 / 234
页数:12
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