Incomplete Fuzzy Preference Matrix and Its Application to Ranking of Alternatives

2014 ◽  
Vol 29 (8) ◽  
pp. 787-806 ◽  
Author(s):  
Jaroslav Ramík
Keyword(s):  
Ranking Of Alternatives ◽  
Fuzzy Preference Matrix ◽  
Preference Matrix ◽  
Fuzzy Preference

scholarly journals Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Abba Suganda Girsang ◽  
Chun-Wei Tsai ◽  
Chu-Sing Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Model Comparison ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Deviation Matrix ◽  
Multiobjective Particle Swarm Optimization ◽  
Hierarchy Process ◽  
Fuzzy Preference Matrix ◽  
Preference Matrix ◽  
Fuzzy Preference

This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions.

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.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

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