Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows

TL;DR

This paper introduces a deep reinforcement learning approach using attention models and graph embeddings to efficiently solve large-scale electric vehicle routing problems with time windows, outperforming existing methods.

Contribution

It presents a novel end-to-end deep reinforcement learning framework with attention mechanisms for EV routing with time windows, capable of handling large problem instances.

Findings

Successfully solves large EVRPTW instances beyond current methods.

Uses policy gradient with rollout baseline for training.

Achieves efficient and scalable solutions.

Abstract

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.