AN APPROACH TO GROUP DECISION MAKING BASED ON INCOMPLETE FUZZY PREFERENCE RELATIONS

2008 ◽  
Vol 16 (01) ◽  
pp. 83-94 ◽  
Author(s):  
YAN-PING JIANG ◽  
ZHI-PING FAN
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
New Approach ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Ranking Of Alternatives ◽  
Collective Ranking ◽  
Fuzzy Preference

In this paper, a new approach is proposed to solve group decision making (GDM) problems where the preference information on alternatives provided by decision makers (DMs) is represented in incomplete fuzzy preference relations. In order to make the collective opinion close each decision maker's opinion as near as possible, an optimization model is constructed to integrate the incomplete fuzzy preference relations and to compute the collective ranking values of alternatives. The ranking of alternatives or selection of the most desirable alternative(s) is directly obtained from the derived collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach.

A Natural Method for Ranking Objects from Hesitant Fuzzy Preference Relations

2017 ◽  
Vol 16 (06) ◽  
pp. 1611-1646 ◽  
Author(s):  
Jie Tang ◽  
Qingxian An ◽  
Fanyong Meng ◽  
Xiaohong Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Group Consensus ◽  
Preference Relations ◽  
Additive Consistency ◽  
Mixed Programming ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Hesitant fuzzy preference relations (HFPRs) are efficient tools to denoting the decision maker’s judgements that permit the decision makers to compare objects using several values in [0, 1], and the number of elements in different hesitant fuzzy elements may be different. After reviewing the previous researches about decision making with HFPRs, one can find that there are several limitations. To avoid these issues and to guarantee the reasonable ranking order, this paper introduces a new additive consistency concept for HFPRs. Different from the previous consistency concepts, the new concept neither needs to add values into hesitant fuzzy elements nor disregards any information offered by the decision makers. To measure the additive consistency of HFPRs, two 0-1 mixed programming models are constructed. Meanwhile, an additive consistency based 0-1 mixed programming model is established to determining the missing values in incomplete HFPRs that can address the situation where ignored objects exist. Then, an algorithm to obtaining the hesitant fuzzy priority weight vector from (incomplete) HFPRs is provided. Considering group decision making, a new group consensus index is defined, and an interactive approach to improving the group consensus level of individual HFPRs is offered. Furthermore, a probability distance measure between two HFPRs is defined to deriving the weights of the decision makers. According to the additive consistency and consensus analysis, an approach to group decision making with incomplete and inconsistent HFPRs is performed. Finally, two practical numerical examples are provided, and comparison analysis is offered.

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