A new consensus model for group decision making using fuzzy linguistic preference relations with heterogeneous experts

Abstract

Group decision making (GDM) problems consist of finding the most acceptable solution based on a group of experts’ preferences. Sometimes, the experts experience difficulty expressing their preferences using crisp values or making comparisons between each pair of alternatives. Meanwhile, because of their different backgrounds or knowledge concerning a specific problem, the opinions of different experts may carry different weights along the decision process. Thus, a new consensus model is proposed to solve these problems. First, experts are required to express their opinions using fuzzy linguistic preference relations, and then, a new method is proposed for classifying the experts into three different importance levels according to these opinions. Then, consistency measures and proximity measures are used to guide the decision-making process. A new feedback mechanism that generates advice for experts according to their different levels is proposed. The importance degrees of experts are taken into consideration throughout the process, which is one of the main novelties of this model. Finally, a numerical example is conducted to illustrate the utilization and compared results are also presented to check the feasibility of the proposed model.