Optimizing Air Cargo Task Allocation with Priority and Disturbance Awareness

This study develops an optimization model for air cargo task allocation that explicitly incorporates priority levels and disturbance awareness. Considering real-world uncertainties such as delayed arrivals and fluctuating processing times, the model supports parallel task execution across multiple devices. A simulated annealing algorithm is applied to generate near-optimal task sequences that minimize total weighted waiting time while preserving fairness and priority compliance. Experimental results demonstrate that the proposed approach maintains robust performance under both ideal and disturbed conditions, effectively reducing delays and improving the responsiveness to high-priority tasks. The findings provide practical insights into intelligent scheduling and resilient resource management for modern air logistics systems.