A new hesitant fuzzy linguistic approach for multiple attribute decision making based on Dempster–Shafer evidence theory

Abstract

Hesitant fuzzy linguistic term sets (HFLTSs) are useful tool to represent qualitative information in multiple attribute decision making (MADM), and Dempster–Shafer evidence theory (DSET) has some advantages in denoting and fusing uncertain information. The goal of this paper is to develop a new hesitant fuzzy linguistic (HFL) MADM approach based on the DSET. To realize this goal, we propose a method of converting the original decision matrix expressed by HFLTSs into the evidence matrix with HFLTSs, and develop a weight-determining model for MADM problems with HFL information. Further, in order to integrate the evidences with HFLTSs under all attributes, we propose a combination algorithm for MADM problems based on the combination rule of DSET. Based on these studies, we develop a HFL-DSET approach for MADM problems with unknown weights. Furthermore, an applicable example for supplier selection is used to illustrate the proposed approach. Lastly, some comparative analyses with other HFL-MADM methods are conducted to show the feasibility and superiority of the proposed approach.