A new approach to deal with consistency and consensus issues for hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic preference relations (HFLPRs) as an efficient and common tool to deal with decision-making problems have been widely used in real life. The consistency and consensus are the most two important topics for HFLPRs. In this paper, we develop a new efficient consistency-consensus framework for HFLPRs. Firstly, we transform HFLPRs into probabilistic linguistic preference relations, and develop a programming model to make the HFLPRs achieve the maximum consistency degree. Then, we propose some new rules for addition operation and weighted average operator to fuse individual preference information. After that, a maximum consensus model is developed which maximizes the consensus degree by adjusting the experts' weights. Then, a numerical case of temporary placement selection after earthquake is presented, and some comparisons with the traditional methods are conducted to verify the rationality and efficiency of our framework. Finally, we end the paper with some conclusions and future research directions. (C) 2018 Elsevier B.V. All rights reserved.