  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.
