مهديس مقدم

Access to healthcare is a fundamental human right; however, global health systems are increasingly constrained by economic, social, an‎d environmental challenges. This research addresses the critical issue of pharmaceutical waste by proposing a multi-echelon reverse supply chain (RSC) network for the redistribution of near-expiry medications, such as antibiotics, from retail pharmacies to charitable clinics. The objective is twofold: to minimize resource depletion an‎d to enhance healthcare accessibility for underprivileged communities. The network structure comprises three distinct echelons: pharmacies (collection points), an intermediate distribution center, an‎d charitable clinics (deman‎d nodes). To improve computational efficiency an‎d network architecture, an accelerated K-means clustering algorithm is initially employed. Subsequently, a bi-objective mixed-integer linear programming (MILP) model is formulated to integrate network design with inventory-routing decisions. The first objective minimizes total logistics costs, while the second maximizes social impact by optimizing job creation within the network. To address inherent parameter uncertainty in supply an‎d deman‎d, a scenario-based robust optimization approach is adopted. The Augmented ϵ-constraint method is implemented to generate a comprehensive set of Pareto-optimal solutions. The proposed methodology combines clustering techniques, robust mathematical optimization solved via GAMS, an‎d sensitivity analysis. Empirical findings demonstrate that the implementation of the clustering strategy achieves a 15–20% reduction in logistical costs. By addressing the research gap in the reverse logistics of perishable pharmaceuticals, this study provides policy-makers with a robust, scalable framework for sustainable pharmaceutical distribution.

زنجيره تأمين دارو , داروهاي در حال انقضا , درمانگاه‌هاي خيريه , مدل رياضي چند هدفه , برنامه‌ريزي استوار

Pharmaceutical Supply Chain , Near-expiry Pharmaceuticals , Charitable Healthcare , Multi-Objective Mathematical Model , Robust Optimization

Mahdis Moqaddam

Dr. Ahmad Makuee