Dynamic truck-drone routing problem for multiple tasks in post-disaster scenarios

This study investigates a dynamic routing problem of a truck-and-drone system for delivery and surveillance tasks in post-disaster scenarios. To maximize the number of rescued people under uncertainties, the route of trucks and drones need to keep updating. A reinforcement learning method is developed to solve the proposed problem.