Interactive algorithms for normalized probabilistic linguistic preference relations in view of the disjunctive probability based consistency and consensus analysis

摘要

This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones.