Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows

LECTRIC vehicles (EV) have been playing an increasingly important role in urban transportation and logistics tackle CO even without optimal labels. They consider solving systems for their capability of reducing greenhouse gas emission, problems through taking a sequence of actions similar to promoting renewable energy and introducing sustainable Markov decision process (MDP). Some reward schemes are transportation system [1], [2]. To model the operations of designed to inform the model about the quality of the actions logistic companies using EVs for service provision, Schneider it made based on which model parameters are adjusted to et al. proposed the electric vehicle routing problem with time enhance the solution quality. It has already been successfully windows (EVRPTW) [3]. In the context of EVRPTW, a fleet applied to various COs such as the travelling salesman problem of capacitated EVs is responsible for serving customers located (TSP), vehicle routing problem (VRP), minimum vertex cover in a specific region; each customer is associated with a demand (MVC), maximum cut (MAXCUT) etc. Despite the difficulty that must be satisfied during a time window; all the EVs are in training deep RL models, it is currently accepted as a very fully charged at the start of the planning horizon and could promising research direction to pursue.