A Theoretical Analysis of Fuzzy MCDM Methods for Healthcare Resource Allocation Under Uncertainty

Abstract

Healthcare systems face critical challenges in resource allocation and decision-making under uncertainty. This study applies three fuzzy multi-criteria decision-making (MCDM) methods-Fuzzy AHP, Fuzzy TOPSIS, and Fuzzy OWA-to address these challenges. Each method is mathematically analyzed and applied to a case study involving the allocation of limited medical resources across patient groups, evaluated by severity of condition, treatment cost, and expected benefit. The results show that Fuzzy AHP supports hierarchical decisions but is computationally intensive; Fuzzy TOPSIS provides intuitive rankings but is sensitive to weight accuracy; and Fuzzy OWA models risk preferences flexibly but requires careful aggregation weighting. Comparative analysis highlights the methods' complementary strengths and suitability for healthcare contexts. The study contributes to the development of effective decision-support systems under uncertainty. Future directions include hybrid approaches and integration with real-time data for broader applicability.