Fleet Scheduling

Fleet scheduling optimizes the allocation and routing of vehicles to fulfill transportation demands efficiently, minimizing costs and maximizing service levels. Current research heavily utilizes graph-based methods, including graph neural networks and reinforcement learning (often combined with imitation learning or Bayesian optimization), to address the computational complexity of real-world scenarios like urban air mobility and on-demand services. These advancements are crucial for improving the efficiency and sustainability of various transportation systems, from logistics and delivery services to autonomous robot fleets and urban air mobility networks. The development of efficient and robust algorithms is particularly important for handling uncertainties in demand, weather, and vehicle availability.