Abstract
Pythagorean fuzzy sets (PFSs) retain the advantages of intuitionistic fuzzy sets (IFSs), while PFSs portray 1.57 times more information than IFSs. In addition, Pythagorean fuzzy preference relations (PFPRs), as a generalization of intuitionistic fuzzy preference relations (IFPRs), are more flexible and applicable. The objective of this paper is to propose a novel decision support model for solving group decision-making problems in a Pythagorean fuzzy environment. First, we define the concepts of ordered consistency and multiplicative consistency for PFPRs. Then, aiming at the group decision-making problem of multiple PFPRs, a consistency improving model is constructed to improve the consistency of group preference relations. Later, a consensus reaching model is developed to reach the degree of group consensus. Furthermore, a decision support model with PFPRs is established to derive the normalized weights and output the final result. Holding these features, this paper builds a decision support model with PFPRs based on multiplicative consistency and consensus. Finally, the described method is validated by an example of financial risk management, and it is concluded that the solvency of a company is an important indicator that affects the financial early warning system.
Highlights
Decision-making implies that there is a range of choices
One reason is that DMs may not have an accurate or sufficient knowledge level of the problem, and the other is that DMs cannot clearly distinguish the extent to which one is superior to others [7, 8]
Compared with the method proposed by He et al [39], we find that the results obtained by the method in [39] are different from those obtained by our proposed method
Summary
Decision-making implies that there is a range of choices. In this case, decision-makers (DMs) rank these choices from best to worst, and choose a goal that meets the expectations of DMs. One reason is that DMs may not have an accurate or sufficient knowledge level of the problem, and the other is that DMs cannot clearly distinguish the extent to which one is superior to others [7, 8]. In these cases, DMs may prefer to compare the imprecise judgment information in a matrix in pairs. DMs may prefer to compare the imprecise judgment information in a matrix in pairs To describe this vagueness and indeterminacy, researchers have introduced fuzzy sets (FSs) [9] theory to different preference relations (PRs).
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