A robust routing approach for truck-UAV collaborative emergency supply delivery under uncertain wind conditions

Abstract

Efficient emergency distribution is critical for European disaster response. In Europe, although truck-UAV systems provide a flexible solution to logistics bottlenecks, the continent’s coastal regions and expansive plains experience highly volatile wind conditions, which expose UAV operations to substantial uncertainties in wind dynamics and energy consumption. By incorporating a novel wind risk penalty term and an urgency-aware prioritization scheme, the proposed model ensures both mission stability and response timeliness. We develop four unique operators and integrate them into a modified Greedy Randomized Adaptive Search Procedure (GRASP)-Adaptive Large Neighborhood Search (ALNS) framework to adapt to the uncertain wind conditions and complex energy consumption. Experimental results based on transformed benchmark scenarios demonstrate that the proposed hybrid algorithm yields a 6.68% performance gain over Pure ALNS baseline. Notably, the model achieves a 100% compliance rate for the 94% coverage target across all tested instances while maintaining highly stable target objective values under wind fluctuations, serving as a practical decision-support tool for emergency logistics operations in wind-sensitive environments.