Drone-Enhanced Last-Mile Medical Delivery in Rural Areas: A Weather-Aware Optimization Approach

Rural communities face persistent challenges in accessing timely and reliable medical supply delivery due to infrastructure limitations and dispersed populations. This study presents a weather-aware, multi-modal optimization framework that integrates drones and ground vehicles to enhance last-mile healthcare logistics. The model accounts for vehicle routing, depot and facility siting, time-sensitive delivery, transshipment, and recharging decisions under operational and environmental constraints. Drone energy consumption is estimated using a dynamic model that incorporates local weather conditions and temporal variations. We develop multiple case study instances based on real-world data from rural Southern Illinois and evaluate the model’s performance across various delivery configurations, including ground-only, drone-only, and hybrid networks. Results show that drone-only configurations reduce costs by up to 47% in low-volume, dispersed settings. In high-volume cases—defined by dense demand clusters and larger total order weights—hybrid solutions significantly improve delivery efficiency, cutting average delivery time by more than 50% while preserving operational feasibility. Compared to simplified range-based models commonly used in the literature, the proposed approach more accurately captures energy demands and enables better-informed routing decisions—offering a robust platform for navigating trade-offs between cost, timeliness, and network complexity in rural healthcare logistics.