An Optimal Algorithm of Group Decision Making Based on Linguistic Fuzzy Preference Information

Author(s):  
Zeng Xuelan ◽  
Li Zhengyi
Author(s):  
Huchang Liao ◽  
Zeshui Xu

Intuitionistic fuzzy preference relation has turned out to be a powerful structure in representing the decision makers' preference information especially when the decision makers are not able to express their preferences accurately due to the unquantifiable information, incomplete information, unobtainable information, partial ignorance, and so forth. The aim of this paper is to develop some techniques for group decision making with intuitionistic fuzzy preference information. Based on the multiplicative consistency of intuitionistic fuzzy preference relation, three algorithms are proposed for intuitionistic fuzzy group decision making. In the case that the decision makers act as separate individuals, the priority vector of each decision maker can be derived directly from the individual intuitionistic fuzzy preference relation, after which an overall priority vector is obtained by synthesizing those individual priorities together. As for the scenario that the decision makers act as one individual, two different algorithms based on the multiplicative consistency are proposed to deal with this case. The main idea of the former procedure is firstly constructing a social intuitionistic fuzzy preference relation, while that of the later is building a fractional programming model. Some practical examples are given to demonstrate the developed algorithms.


2014 ◽  
Vol 13 (05) ◽  
pp. 979-1012 ◽  
Author(s):  
Ting-Yu Chen

Interval type-2 fuzzy sets (T2FSs) with interval membership grades are suitable for dealing with imprecision or uncertainties in many real-world problems. In the Interval type-2 fuzzy context, the aim of this paper is to develop an interactive signed distance-based simple additive weighting (SAW) method for solving multiple criteria group decision-making problems with linguistic ratings and incomplete preference information. This paper first formulates a group decision-making problem with uncertain linguistic variables and their transformation to interval type-2 trapezoidal fuzzy numbers. Concerning the relative importance of multiple decision-makers and group consensus of fuzzy opinions, a procedure using hybrid averages is then employed to construct a collective decision matrix. By an appropriate extension of the classical SAW approach, this paper utilizes the concept of signed distances and establishes an integrated programming model to manage multi-criteria group decisions under the incomplete and inconsistent preference structure. Further, an interactive procedure is established for group decision making. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a collaborative decision-making problem of patient-centered care (PCC).


2021 ◽  
pp. 1-23
Author(s):  
Ziyu Yang ◽  
Liyuan Zhang ◽  
Tao Li

Interval-valued Pythagorean fuzzy preference relation (IVPFPR) plays an important role in representing the complex and uncertain information. The application of IVPFPRs gives better solutions in group decision making (GDM). In this paper, we investigate a new method to solve GDM problems with IVPFPRs. Firstly, novel multiplicative consistency and consensus measures are proposed. Subsequently, the procedure for improving consistency and consensus levels are put forward to ensure that every individual IVPFPR is of acceptable multiplicative consistency and consensus simultaneously. In the context of minimizing the deviations between the individual and collective IVPFPRs, the objective experts’ weights are decided according to the optimization model and the aggregated IVPFPR is derived. Afterwards, a programming model is built to derive the normalized Pythagorean fuzzy priority weights, then the priority weights of alternatives are identified as well. An algorithm for GDM method with IVPFPRs is completed. Finally, an example is cited and comparative analyses with previous approaches are conducted to illustrate the applicability and effectiveness of the proposed method.


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