A C-OWA operator-based approach to decision making with interval fuzzy preference relation

2006 ◽  
Vol 21 (12) ◽  
pp. 1289-1298 ◽  
Author(s):  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Preference Relation ◽  
Owa Operator ◽  
Fuzzy Preference Relation ◽  
Interval Fuzzy Preference Relation ◽  
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.

scholarly journals A NOTE ON THE ESTIMATION OF MISSING PAIRWISE PREFERENCE VALUES: A UNINORM CONSISTENCY BASED METHOD

2008 ◽  
Vol 16 (supp02) ◽  
pp. 19-32 ◽  
Author(s):  
FRANCISCO CHICLANA ◽  
ENRIQUE HERRERA-VIEDMA ◽  
SERGIO ALONSO ◽  
FRANCISCO HERRERA
Keyword(s):  
Decision Making ◽  
Incomplete Information ◽  
Missing Values ◽  
Preference Relation ◽  
Estimation Method ◽  
Short Discussion ◽  
Fuzzy Preference Relation ◽  
Pairwise Preference ◽  
Incomplete Fuzzy Preference Relation ◽  
Fuzzy Preference

Dealing with incomplete information is an important problem in decision making. In this paper, we present a short discussion on this topic and a new estimation method of missing values in an incomplete fuzzy preference relation which is based on the modelling of consistency of preferences via a representable uninorm.

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