A large-scale group decision making method with a consensus reaching process under cognitive linguistic environment

Group decision making (GDM) problems involving large-scale experts are critical and frequent in practice. However, it is difficult to make an appropriate decision due to the disagreement among experts and the uncertain information of experts because of their different backgrounds and limited knowledge. This study aims to propose a large-scale GDM method with a consensus reaching process under the cognitive linguistic environment. First, we divide the large-scale group into several subgroups to simplify the problem. Then, the double normalization based multiaggregation (DNMA) method is extended to deal with the individual decision matrix of each subgroup that is composed of complex linguistic evaluations. In addition, a consensus reaching process is proposed to check the consensus level between subgroups and achieve a soft consensus through a consensus improvement method. A dynamic weight-determining method is developed for subgroups based on their consensus levels. Finally, the framework of the large-scale group DNMA method with a consensus reaching process is constructed. The practicality of the proposed method is illustrated by an illustrative example about the e-book supplier selection, and the advantages of the proposed method are highlighted by comparative analyses with other decision-making methods.