Selecting a health emergency strategy through large-scale multi-criteria decision-making based on intuitionistic fuzzy self-confidence data

Abstract

In complex decision-making scenarios involving multiple stakeholders, the uncertainty and individual confidence of decision-makers (DMs) are crucial in determining the outcomes. A novel approach is proposed in this paper to improve decision-making processes within a large group of DMs operating under an “Intuitionistic Fuzzy Self-Confidence (IFN-SC)” setting. The research presents a hybrid clustering algorithm to categorize DMs based on their numerical similarities and psychological factors. A multi-objective nonlinear optimization problem is employed to determine the criteria weights in the IFN-SC environment when the weight vector is either partially or fully unknown. Using the max operator, we derive a single-objective nonlinear optimization problem, which is solved by the “Particle Swarm Optimization (PSO)” algorithm. Furthermore, extending the “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)” for the IFN-SC environment significantly enhances the model’s effectiveness in ranking alternatives. The study exemplified its capability in managing a large-scale decision-making problem based on health emergency strategy selection and presented various analyses highlighting its utility, adaptability, and robustness in practical situations.