Multiplicative Consistency Adjustment Model and Data Envelopment Analysis-Driven Decision-Making Process with Probabilistic Hesitant Fuzzy Preference Relations

Unlike other fuzzy modellings, probabilistic fuzzy sets can reflect clearly the importance of different numerical values. In group decision-making (GDM) problems, it is quite common for decision-makers (DMs) to elicit their knowledge with probabilistic hesitant fuzzy preference relations (PHFPRs), in which consistency adjustment and alternatives' weight vector determination play a key role in the decision-making process. This study aims at constructing a decision-making model with PHFPRs. First, several new concepts are introduced, including the multiplicative consistency and consistency index of PHFPRs. Then, we present a construction approach for the multiplicative consistent PHFPRs, and a convergent local consistency improvement process for PHFPRs is designed to detect and improve their consistency when the PHFPRs do not meet the consistency level. The local adjustment strategy is utilized to retain the preference evaluation of DMs as much as possible. Afterwards, based on the obtained efficiency score values, we propose a new data envelopment analysis model to derive the weight values of alternatives. Furthermore, we explore a decision-making method with PHFPRs to obtain the optimal selection from the alternatives. Finally, an applied case about logistics company assessment is presented, and the effectiveness and rationality of the explored method are verified by the comparison with the various approaches.