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Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making

2014; Elsevier BV; Volume: 297; Linguagem: Inglês

10.1016/j.ins.2014.11.011

1872-6291

Yucheng Dong, Xia Chen, Francisco Herrera,

Rough Sets and Fuzzy Logic

In some real-world decision processes, decision makers may prefer to provide their opinions using linguistic expressions instead of a single linguistic term. Particularly, they may hesitate between several linguistic terms. In this paper, we deal with the consensus issue in the hesitant linguistic group decision making (GDM) problem. Firstly, a novel distance-based consensus measure is proposed. Then, using this consensus measure we develop an optimization-based consensus model in the hesitant linguistic GDM, which minimizes the number of adjusted simple terms in the consensus building. Furthermore, a two-stage model is displayed to further optimize the solutions to the proposed consensus model, through which we obtain the unique optimal adjustment suggestion to support the consensus reaching process in the hesitant linguistic GDM. Finally, several desirable properties are proposed to justify the proposal, and two examples are used to demonstrate the validity of the models.