# Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows

arXiv: https://arxiv.org/abs/2010.02068 · HTML: https://mlsanity.com/paper/2010.02068/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives](https://mlsanity.com/paper/2406.00415/) (2024)
- [An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem](https://mlsanity.com/paper/2107.07076/) (2021)
- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem](https://mlsanity.com/paper/2206.03185/) (2022)
- [Learning for routing: A guided review of recent developments and future directions](https://mlsanity.com/paper/2507.00218/) (2025)
- [Learning to Solve Soft-Constrained Vehicle Routing Problems with Lagrangian Relaxation](https://mlsanity.com/paper/2207.09860/) (2022)
- [Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II](https://mlsanity.com/paper/2407.13113/) (2024)
- [Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey](https://mlsanity.com/paper/2108.04462/) (2021)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints](https://mlsanity.com/paper/2407.01615/) (2024)
- [Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey](https://mlsanity.com/paper/2508.20315/) (2025)
- [Chain-of-Context Learning: Dynamic Constraint Understanding for Multi-Task VRPs](https://mlsanity.com/paper/2603.01667/) (2026)
- [Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands](https://mlsanity.com/paper/2109.10200/) (2021)
- [Multi-Agent Path Planning Using Deep Reinforcement Learning](https://mlsanity.com/paper/2110.01460/) (2021)
- [A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply](https://mlsanity.com/paper/2204.05545/) (2022)
- [A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem](https://mlsanity.com/paper/2601.15038/) (2026)
- [Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems](https://mlsanity.com/paper/2606.10431/) (2026)
- [Learning to Search for Vehicle Routing with Multiple Time Windows](https://mlsanity.com/paper/2505.23098/) (2025)
- [Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem](https://mlsanity.com/paper/2506.01065/) (2025)
- [Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem](https://mlsanity.com/paper/2512.24251/) (2025)
- [Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding](https://mlsanity.com/paper/2601.15131/) (2026)
- [SEAFormer: A Spatial Proximity and Edge-Aware Transformer for Real-World Vehicle Routing Problems](https://mlsanity.com/paper/2601.19395/) (2026)
- [Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems](https://mlsanity.com/paper/2603.10053/) (2026)
- [Learning to Route Electric Trucks Under Operational Uncertainty](https://mlsanity.com/paper/2604.26566/) (2026)

## Abstract (arXiv)

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.