The consistency discretization adjustment method of interval fuzzy preference relationship based on individual agreement and group consensus

Constrained by the limited knowledge and fuzzy cogitation, it is often difficult to guarantee the consistency of decision makers' preference judgments. To effectively avoid the distortion of information about the original preferences, a discrete scale consistency adjustment model based on integer variables under the individual interval fuzzy preference relationship is established. The research shows that the proposed method can not only validly enhance the differentiation between alternatives, but also drop the occurrence of zero weights to a larger extent. Furthermore, aiming at the potential multiplicity of optimal adjustment strategies for interval fuzzy preference relations, an efficient algorithm for screening multiple optimal solutions under single-person decision making is proposed by setting multiple screening dimensions. The algorithm makes up for the deficiency of ignoring potential optimal solutions in traditional methods, and greatly raises the decision flexibility of the ranking optimization process. The validity and applicability of the algorithm are verified through filtering and analyzing multiple optimal solutions under different scenarios. Considering the differences in the decision making tendencies of exclusive and altruistic groups in the interval fuzzy environment, a discrete compatible adjustment model of group consensus under different decision making behaviors is established. Finally, the efficient screening system is matched for different groups, which provides a valid path for the optimal solution selection of group consensus.