A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients’ Prioritization

Abstract

Patients’ prioritization is a complex multi-criteria decision-making process in the hospital management. The linguistic variable is a preferable way for health experts to evaluate patients. In addition, experts may provide a rich linguistic expression or some linguistic terms in healthcare decision-making processes. Compared to the traditional fuzzy tools, hesitant fuzzy linguistic term sets (HFLTSs) have better applicability in quantifying such linguistic evaluation information. This paper establishes a projection-based multi-attributive border approximation area comparison (MABAC) method with HFLTSs and demonstrates its use in the context of patients’ prioritization. In the novel MABAC method, the classical MABAC method is integrated with the projection-based difference measure, which is defined to depict the difference between two HFLTSs comprehensively. Moreover, the Bonferroni mean is also included in the extended MABAC method to reduce the influence of inherent interdependence among criteria. To explain the effectiveness of the proposed method, we conduct a practical case study that is related to patients’ prioritization in a hospital of China. Furthermore, a sensitivity analysis and a comparative analysis are carried out to verify the rationality and stability of the proposed method.