A CONSENSUS MODEL FOR GROUP DECISION-MAKING PROBLEMS WITH INTERVAL FUZZY PREFERENCE RELATIONS

2012 ◽  
Vol 11 (04) ◽  
pp. 709-725 ◽  
Author(s):  
J. M. TAPIA GARCÍA ◽  
M. J. DEL MORAL ◽  
M. A. MARTÍNEZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Preference Relation ◽  
Feedback Mechanism ◽  
Consensus Model ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Interval fuzzy preference relations can be useful to express decision makers' preferences in group decision-making problems. Usually, we apply a selection process and a consensus process to solve a group decision situation. In this paper, we present a consensus model for group decision-making problems with interval fuzzy preference relations. This model is based on two consensus criteria, a consensus measure and a proximity measure, and also on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process. We show an application example in social work.

A Consensus Model for Group Decision Making With Incomplete Fuzzy Preference Relations

2007 ◽  
Vol 15 (5) ◽  
pp. 863-877 ◽  
Author(s):  
Enrique Herrera-Viedma ◽  
Sergio Alonso ◽  
Francisco Chiclana ◽  
Francisco Herrera
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Consensus Model ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

scholarly journals Compatibility measurement-based group decision making with interval fuzzy preference relations

2016 ◽  
Vol 11 (1) ◽  
pp. 31-39
Author(s):  
Xue-Yang Zhang ◽  
Zhou J Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relation ◽  
Fuzzy Preference Relation ◽  
Interval Numbers ◽  
Preference Relations ◽  
Interval Fuzzy Preference Relation ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

In this paper, we put forward a ratio-based compatibility degree between any two ]0,1[-valued interval numbers to measure how proximate they approach to each other. A compatibility measurement is presented to evaluate the compatibility degree between a pair of ]0,1[-valued interval fuzzy preference relations (IFPRs). By employing the geometric mean, a measurement formula is proposed to calculate how close one interval fuzzy preference relation is to all the other interval fuzzy preference relations in a group. We devise an induced interval fuzzy ordered weighted geometric (IIFOWG) operator to aggregate ]0,1[-valued interval numbers, and apply the induced interval fuzzy ordered weighted geometric operator to fuse interval fuzzy preference relations into a collective one. Based on the compatibility measurement between two interval fuzzy preference relations, a notion of acceptable consensus of interval fuzzy preference relations is introduced to check the consensus level between an individual interval fuzzy preference relation and a collective interval fuzzy preference relation, and a novel procedure is developed to handle group decision-making problems with interval fuzzy preference relations. A numerical example with respect to the evaluation of e-commerce websites is provided to illustrate the proposed procedure.

scholarly journals Geometric Least Square Models for Deriving[0,1]-Valued Interval Weights from Interval Fuzzy Preference Relations Based on Multiplicative Transitivity

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Xuan Yang ◽  
Zhou-Jing Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relation ◽  
Least Square ◽  
Fuzzy Preference Relation ◽  
Preference Relations ◽  
Interval Fuzzy Preference Relation ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

This paper presents a geometric least square framework for deriving[0,1]-valued interval weights from interval fuzzy preference relations. By analyzing the relationship among[0,1]-valued interval weights, multiplicatively consistent interval judgments, and planes, a geometric least square model is developed to derive a normalized[0,1]-valued interval weight vector from an interval fuzzy preference relation. Based on the difference ratio between two interval fuzzy preference relations, a geometric average difference ratio between one interval fuzzy preference relation and the others is defined and employed to determine the relative importance weights for individual interval fuzzy preference relations. A geometric least square based approach is further put forward for solving group decision making problems. An individual decision numerical example and a group decision making problem with the selection of enterprise resource planning software products are furnished to illustrate the effectiveness and applicability of the proposed models.

A Large Group Decision-Making Method Based on Fuzzy Preference Relation

2017 ◽  
Vol 16 (03) ◽  
pp. 881-897 ◽  
Author(s):  
Shenghai Zhou ◽  
Xuanhua Xu ◽  
Yanju Zhou ◽  
Xiaohong Chen
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relation ◽  
Fuzzy Preference Relation ◽  
Preference Relations ◽  
Large Group ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Aiming at the large group decision-making problem in which every decision maker compares pairwise alternatives with fuzzy preference relations, this paper proposes a fuzzy preference relation decision-making method of large group based on conflicts. Firstly, priority should be given to the preference difference under complex large group environment, so we define the conflict degree of two fuzzy preference relations, which contributes to cluster analysis on preferences of the decision maker and thus forms several different clusters. Based on this, we simulate and analyze the threshold of conflict degree. Then we develop the entropy weight method to get the relevant weight of each cluster, and use the weight to aggregate the cluster preferences in order to attain the large group preference based on fuzzy preference relation. Next, an iteration algorithm is introduced to find a solution which could acquire the group alternatives fuzzy preference relation of a certain conflict level and obtain the ranking result of alternatives. Finally, a case analysis is given to illustrate the effectiveness of the method proposed.

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