A shadowed set-based TODIM method and its application to large-scale group decision making

Abstract

As an effective tool to describe the fuzziness of qualitative information, the shadowed sets have received increasing attention. By establishing reasonable compromise between qualitative information and quantitative membership grades, the shadowed sets provide a new way to model linguistic variables. In this paper, we propose an extended TODIM method based on shadowed sets to solve large-scale group decision making problems with linguistic information. First, considering the linguistic terms cannot be directly computed, a codebook used to model linguistic terms is constructed with shadowed sets by the data-driven and the percentile method. Next, to improve the decision efficiency, a shadowed set-based clustering model is proposed to cluster the decision makers on the basis of new similarity measure and the Louvain algorithm. Then, we propose a new decision-making method by taking into account the decision makers’ psychological behavior which has a vital influence on the decision results. Finally, we utilize a case study about assembly factory site selection to illustrate the feasibility of the proposed method, meanwhile comparison, parameter discussion and sensitivity analysis are also conducted to verify the superiority and the efficiency.