Optimizing healthcare supply chain capacity planning during disasters using fuzzy AHP and TOPSIS methods

Abstract

This paper applies Fuzzy AHP and Fuzzy TOPSIS to the problem of healthcare supply-chain capacity planning under uncertainty. We define a practical set of criteria-patient demand, resource availability, cost, and quality of care and use fuzzy pairwise judgments to estimate criterion weights, followed by Fuzzy TOPSIS to rank capacity options (e.g., reallocating staff, adding beds, outsourcing services). Through scenario-based comparisons with crisp AHP/TOPSIS, we show that fuzzy models capture vagueness in expert input, yield rankings that are more stable to small judgment changes, and remain computationally manageable. We provide an implementation walkthrough from criteria design to sensitivity analysis, and summarize observed improvements in decision robustness alongside implications for patient flow and cost control. The findings offer a clear, practice-ready playbook for hospitals seeking to align resources with fluctuating demand while keeping patient impact front and center.