A Two-Stage Greedy Genetic Algorithm for Simultaneous Delivery and Monitoring Tasks with Time Windows - Mingyang Tang; Jiaying Sun; Rongyang Zou (Drones)
With advancements in drone driving technology, drones can now collaborate with trucks to execute tasks. However, existing drone–truck collaborative systems are limited to single-task objectives and lack efficiency in large-scale multi-task scenarios. Enhancing the efficiency of drone–truck cooperative systems necessitates the coordination of drone and truck paths to execute multiple tasks simultaneously. Addressing time conflicts in such scenarios remains a significant challenge. This study proposes an innovative drone–truck collaborative system enabling the concurrent execution of delivery and monitoring tasks within specified time windows. To minimize travel costs, a two-stage greedy genetic algorithm (TGGA) is introduced. The methodology initially separates tasks, processes them in batches, and subsequently recombines them to determine the final route. The simulation results indicate that TGGA outperforms existing heuristic algorithms.