Unmanned Aerial Vehicle Delivery Route Planning in Cold Weather Using Neural Combinatorial Optimization and Adaptive Large Neighborhood Search

As the low-altitude economy grows rapidly, unmanned aerial vehicle (UAV) logistics is expanding into cold regions. Such operations must account for the coupled effects of low temperature, wind speed and direction, and decreasing payload on energy consumption and endurance; using conservative or average weather parameters may waste resources or underestimate operational risks. This study proposes a method for multi-UAV, multi-sortie delivery planning in cold regions that minimizes total operating cost while serving all customers and ensuring safe depot returns. The method formulates a multi-sortie energy consumption and endurance model accounting for temperature, wind fields, and payload changes, and couples neural sequence generation conditioned on known weather time series with continuous-physics route splitting and adaptive large neighborhood search to perform customer sequencing, sortie partitioning, and schedule improvement. Across test instances of three customer sizes, schedules generated under different planning conditions are evaluated under the same simulated dynamic weather. Results show that Dynamic planning completes 72/72 benchmark instances and reduces mean penalized cost by 32.1–65.9% compared with Typical planning (which completes 57/72), increases mean completion rate by 16.7–37.5 percentage points across three training seeds, and maintains higher completion rates on both in-range and extreme historical cold days. This study provides a planning method that balances operational feasibility and cost efficiency and quantifies the operational value of weather time-series information.