A Binary Risk Linguistic Fuzzy Behavioral TOPSIS Model for Multi-attribute Large-Scale Group Decision-Making Based on Risk Preference Classification and Adaptive Weight Updating

Abstract

In practical decision-making, linguistic term set is a useful tool to describe the uncertainty and fuzziness of data sources. However, in some decisions, when the data source is unreliable or the decision involves future factors, the evaluation given by the linguistic term set will have a certain degree of error. This paper proposes a binary risk linguistic set based on linguistic term set and R-set. The binary risk linguistic set considers the linguistic term set and the risk factors that may lead to errors in language evaluation. In order to facilitate the use of binary risk linguistic set, the risk conversion function and operational laws are introduced. Next, since group decision-making involves multiple experts, considering the social relations between experts, a method to estimate the missing values in the social network matrix is proposed by utilizing the trust intensity propagation operator and the relationship intensity propagation operator. Risk perception can reflect the subjective judgment of experts on the characteristics and severity of a particular risk, and different judgment results can reflect the attitude of experts to risk. Hereby, this study proposes a risk clustering method based on the risk perception of experts. Furthermore, we propose an adaptive weight updating method based on social network matrix. Then, a binary risk linguistic fuzzy behavioral TOPSIS method is proposed to deal with the multi-attribute large-scale group decision-making (MALSGDM) problem. Finally, a case study is used to demonstrate the feasibility of the presented method, and its effectiveness is validated through comparison with other MALSGDM methods. To demonstrate the effectiveness of the proposed method, this study also perform sensitivity and stability assessments of the decision-makers’ weight and behavior characteristics.