Climate change, urbanization, and environmental degradation have contributed to the growing frequency and severity of natural disasters, including hurricanes, floods, and earthquakes. A persistent challenge in disaster response is the rapid and efficient delivery of humanitarian aid, particularly in areas where ground infrastructure has been damaged or destroyed. Recent advances in drone technology have improved range and payload capacities, making autonomous drones an increasingly viable last-mile delivery solution. Furthermore, the use of electric drones supports sustainability objectives by reducing carbon emissions relative to conventional delivery methods. This study proposes an electric drone-based logistics framework for last-mile humanitarian aid delivery from a central distribution hub to disaster-affected areas. A mixed-integer linear programming (MILP) model is formulated to optimize drone scheduling and coordination, while incorporating constraints related to payload capacity, battery management, travel distance, dynamic departure times, variable travel durations, and demand fulfillment. The objective is to minimize total operational costs and delivery times, improving disaster responsiveness. The proposed model enables efficient and adaptive resource allocation in disrupted environments, improving the speed and reliability of aid delivery. This work contributes to the development of disaster response systems by providing an optimization framework that demonstrates the potential of electric drone technologies to advance resilience and sustainability in humanitarian logistics.

Keywords

Optimization, Scheduling, Drones, Last-Mile Delivery, Disaster Response