Learned Unmanned Vehicle Scheduling for Large-Scale Urban Logistics

The adoption of unmanned vehicles in urban logistics has gradually become a trend. It can effectively lower carbon emissions, reduce labor costs, and improve logistics efficiency. In this paper, we investigate a novel problem of unmanned vehicle scheduling (UVS) for large-scale urban logistics, where the logistics platform assigns unmanned vehicles to deliver parcels among stations under the constraints of time, capacity, and electricity to maximize the overall revenue of the logistics platform. Although the UVS problem is of practical usefulness, solving it requires non-trivial efforts, because we have proved that the UVS problem is NP-hard. To solve the UVS problem efficiently, we propose an efficient two-stage processing framework, including task assignment and vehicle reposition. Specifically, in the first stage, we propose an effective preference-aware matching (PAM) algorithm to deal with task assignments between unmanned vehicles and delivery tasks, which considers not only the electricity consumption of unmanned vehicles but also the supply-demand balance between delivery tasks and unmanned vehicles. In the second stage, we propose two vehicle repositioning algorithms based on deep reinforcement learning, termed restricted DQN repositioning algorithm (RDR) and restricted A2C repositioning algorithm (RAR), which can effectively refine the vehicle's reposition stations based on vehicle supply and demand, electricity supply and demand, charging pile availability and collision avoidance restriction rules at current and neighbor stations, so that the vehicles can be efficiently relocated to stations with over-delivery tasks. Finally, extensive experiments have demonstrated that our proposed algorithms can achieve desirable efficiency and effectiveness.