# Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem

arXiv: https://arxiv.org/abs/2506.01065 · HTML: https://mlsanity.com/paper/2506.01065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2010.02068/) (2020)
- [The Electric Vehicle Routing Problem with Nonlinear Charging Functions](https://mlsanity.com/paper/2108.01273/) (2021)

## Abstract (arXiv)

The Electric Vehicle Routing Problem (EVRP) extends the capacitated vehicle routing problem by incorporating battery constraints and charging stations, posing significant optimization challenges. This paper introduces a Trilevel Memetic Algorithm (TMA) that hierarchically optimizes customer sequences, route assignments, and charging station insertions. The method combines genetic algorithms with dynamic programming, ensuring efficient and high-quality solutions. Benchmark tests on WCCI2020 instances show competitive performance, matching best-known results for small-scale cases. While computational demands limit scalability, TMA demonstrates strong potential for sustainable logistics planning.