Optimizing Drone-Based Medical Supply Delivery in Disaster-Affected Areas: A Case Study from Puerto Rico

Abstract

This paper presents an innovative approach to disaster response using drone-based systems for efficient medical supply delivery post-hurricane in Puerto Rico. By employing heuristic personification algorithms and 0-1 integer programming, our study establishes the optimal deployment of drones and container locations. We utilize three types of drones (H-type, C-type, and B-type) with capacities to carry distinct medical package categories (MED1, MED2, MED3) to strategic containers in Arecibo, San Juan, and Fajardo. The research uses Lingo software for solving the models, ensuring feasible drone missions with return capabilities and meeting varying hospital demands. A Greedy Algorithm is integrated to schedule deliveries, complemented by a transportation model that optimizes the balance between supply locations and demand points for minimal transport distances and timely delivery. The effectiveness of our proposed framework enhances disaster relief operations’ responsiveness and reliability, offering a scalable model for similar global contexts. This research received the Meritorious Winner award in the Mathematical Contest in Modeling (MCM) 2019.