Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows
This addresses routing challenges for logistics companies using electric vehicle fleets, representing an incremental improvement in optimization methods.
The authors tackled the electric vehicle routing problem with time windows (EVRPTW) by proposing an end-to-end deep reinforcement learning framework, which efficiently solves large-scale instances that existing methods cannot handle.
The past decade has seen a rapid penetration of electric vehicles (EV) in the market, more and more logistics and transportation companies start to deploy EVs for service provision. In order to model the operations of a commercial EV fleet, we utilize the EV routing problem with time windows (EVRPTW). In this research, we propose an end-to-end deep reinforcement learning framework to solve the EVRPTW. In particular, we develop an attention model incorporating the pointer network and a graph embedding technique to parameterize a stochastic policy for solving the EVRPTW. The model is then trained using policy gradient with rollout baseline. Our numerical studies show that the proposed model is able to efficiently solve EVRPTW instances of large sizes that are not solvable with any existing approaches.