Informatica

This study proposes an integrated decision support framework for evaluating hospital service quality under uncertainty using Interval-Valued Pythagorean Fuzzy (IVPF) information. It combines an improved entropy measure for expert weighting, WENSLO-RANCOM method for criteria weighting, and an IVPF extension of CRADIS method for hospital ranking. The framework is applied to five hospitals in Patna, India, using four experts and eighteen criteria. Public transport, ambulance services, and medication expertise emerge as the most important factors. Hospital-2 ranks first overall. Sensitivity and comparative analyses confirm the robustness of the results, while the framework also supports resource allocation and service improvement planning.

The objective of the paper is to introduce a novel approach using the multi-attribute border approximation area comparison (MABAC) approach under intuitionistic fuzzy sets (IFSs) to solve the smartphone selection problem with incomplete weights or completely unknown weights. A novel discrimination measure of IFSs is proposed to calculate criteria weights. In view of the fact that the ambiguity is an unavoidable feature of multiple-criteria decision-making (MCDM) problems, the proposed approach is an innovative process in the decision-making under uncertain settings. To express the utility and strength of the developed approach for solving problems in the area of MCDM, a smartphone selection problem is demonstrated. To validate the IF-MABAC approach, a comparative discussion is made between the outcomes of the developed and those of the existing methods. The outcomes of analysis demonstrate that the introduced method is well-ordered and effective with the existing ones.