Abstract:
Logistics and transportation companies have rapidly adopted electric vehicles (EVs) for service delivery over the past decade. The EV routing problem with time windows (EVRPTW) models commercial EV fleet operations. This paper proposes an end-to-end deep reinforcement learning framework for EVRPTW. We parameterize a stochastic EVRPTW policy using an attention model with the pointer network and a graph embedding layer. Policy gradient with rollout baseline trains the model. Our numerical studies show that the proposed model can efficiently solve large EVRPTW instances that current approaches cannot solve.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
