Verifying and managing additive consistency and deriving weights for hesitant fuzzy preference relations

Abstract

Hesitant fuzzy preference relation (HFPR) is a valid tool to describe the hesitation, ambiguity and uncertainty of decision makers. As a crucial criterion to ensure the rationality of preferences and final decision results, the consistency of preference relations is a valuable research topic. In this study, the additive consistency of HFPRs is investigated. Two kinds of consistency, completely additive and weakly additive, for HFPRs are introduced. Some 0–1 mixed programming models and simple algebraic operations are developed to detect the additive consistency type for HFPRs. Further, the priority weights of a consistent HFPR can be derived by constructing and solving two linear programming models. If an HFPR is identified to be inconsistent, we present a straightforward method to rectify inconsistency. Then, an integrated algorithm is proposed to ascertain the additive consistency type, improve consistency and rank alternatives. Finally, the applicability and validity of this proposal are verified through a case study, discussion and comparative analysis with the existing methods.