Enhanced Adaptive Path Optimization for UAV Logistics Delivery in Post-Disaster Scenarios

Post-disaster logistics require rapid delivery under disrupted infrastructure and stringent operational constraints. Unmanned aerial vehicles (UAVs) can reach affected areas efficiently, yet their missions are limited by battery endurance, payload capacity, wind disturbances, and multi-UAV coordination requirements. This paper proposes Enhanced Adaptive Path Optimization (EAPO), a lightweight and deterministic heuristic for coordinated multi-UAV logistics. EAPO integrates (i) return-aware energy feasibility, (ii) non-partial single-visit service, and (iii) priority-weighted node selection into an online decision loop that operates without offline training or instance-specific parameter tuning. Simulation-based evaluations under stochastic wind and heterogeneous demand, involving scenarios with several hundred service nodes and multiple UAVs, demonstrate that EAPO achieves systematic reductions in total operation time (up to 15.3%) and energy consumption (approximately 5.7–12.6%) relative to baseline algorithms, while maintaining competitive average delivery time compared with established routing strategies. Runtime scaling experiments indicate smooth growth with respect to problem size, with collision-avoidance overhead remaining negligible within the evaluated operating regimes. These results position EAPO as an interpretable baseline for mission-level UAV logistics under explicit feasibility and service constraints.