Route Optimization for Autonomous Drone-based Blood Delivery Using a Hybrid Intelligent Algorithm

The emergence of the Low-altitude Economy, driven by Unmanned Aerial Vehicles (UAVs), offers a transformative solution for time-critical logistics, particularly in healthcare. This paper addresses the complex route optimization problem for autonomous drone-based blood delivery, a critical challenge in ensuring rapid emergency medical response. We first formulate the problem as a comprehensive mathematical model, the Multi-Drone Blood Delivery Routing Problem (MD-BDRP), which incorporates realistic operational constraints such as non-linear, payload-dependent energy consumption, vehicle capacity limits, and service time windows. To solve this NP-hard problem, we design and implement a novel hybrid intelligent algorithm, HGA-VNS, which synergizes the global exploration capabilities of a Genetic Algorithm (GA) with the powerful local search of a Variable Neighborhood Search (VNS). Extensive computational experiments, based on a real-world case study in Nanjing, China, are conducted to validate the effectiveness of our approach. The results demonstrate that HGA-VNS significantly outperforms a baseline genetic algorithm in both solution quality and stability. Furthermore, our parametric analysis provides actionable insights into the critical trade-offs between key operational parameters, such as fleet size, total cost, and energy safety thresholds, offering a robust decision-support framework for the practical and economic deployment of autonomous drone delivery systems.