Title: Dynamic Drone-Assisted Pickup and Delivery Routing
Primary area: applications to robotics, autonomy, planning
Keywords: Reinforcement learning, autonomous systems, vehicle routing, optimization

Abstract: We investigate the dynamic drone-assisted pickup and delivery problem (DAPDP), which concerns real-time, on-demand routing decisions in scenarios where new paired orders arrive stochastically throughout the day. By leveraging a fleet of trucks each equipped with a drone, operators can split tasks between ground vehicles and aerial vehicles, aiming to minimize total travel costs while respecting constraints on time windows, capacity, and drone flight endurance. We propose a deep reinforcement learning (DRL) approach based on deep Q-learning, to decide dynamically which newly arrived orders to dispatch and how to integrate drone sorties effectively. Our experiments on a large, real-world-inspired dataset demonstrate substantial performance gains over greedy, random, and lazy dispatch baselines, yielding 10.6\%, 22.6\%, and 37.2\% savings, respectively, in total travel cost. 
Additionally, our value-based RL learns subset selection decisions that co-adapt with a paired sub-solver, yielding near-oracle performance and outperforming classical and PPO baselines.