A Consistency-Driven Approach To Set Personalized Numerical Scales For Hesitant Fuzzy Linguistic Preference Relations

In decision making dealing with computing with words, the importance of the statement that words mean different things for different people has been highlighted. In this paper, we focus on personalizing numerical scales of linguistic terms in decision making with hesitant fuzzy linguistic preference relations (HFLPRs). First, an average consistency measure for HFLPRs is provided, and then an optimization-based model to personalize individual semantics via numerical scales is presented, aiming at maximizing the average consistency of HFLPRs. Numerical examples are used to illustrate the proposal.