RouteFinder: Towards Foundation Models for Vehicle Routing Problems

This repository contains the checkpoints for RouteFinder, a comprehensive foundation model framework designed to tackle various Vehicle Routing Problem (VRP) variants. This model was presented in the paper RouteFinder: Towards Foundation Models for Vehicle Routing Problems.

The official code and detailed instructions are available in the GitHub repository.

Abstract

This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any combination of these attributes. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods. Our code is publicly available at this https URL .

Installation

We use uv (Python package manager) to manage the dependencies:

uv venv --python 3.12 # create a new virtual environment
source .venv/bin/activate # activate the virtual environment
uv sync --all-extras # for all dependencies

Note that this project is also compatible with normal pip install -e . in case you use a different package manager.

Quickstart

Download data and checkpoints

To download the data and checkpoints from HuggingFace automatically, you can use:

python scripts/download_hf.py

Running

We recommend exploring this quickstart notebook to get started with the RouteFinder codebase!

The main runner (example here of main baseline) can be called via:

python run.py experiment=main/rf/rf-transformer-100

You may change the experiment by using the experiment=YOUR_EXP, with the path under configs/experiment directory.

Testing

You may use the provided test function to test the model:

python test.py --checkpoint checkpoints/100/rf-transformer.ckpt

or with additional parameters:

usage: test.py [-h] --checkpoint CHECKPOINT [--problem PROBLEM] [--size SIZE] [--datasets DATASETS] [--batch_size BATCH_SIZE]
               [--device DEVICE] [--remove-mixed-backhaul | --no-remove-mixed-backhaul]

options:
  -h, --help            show this help message and exit
  --checkpoint CHECKPOINT
                        Path to the model checkpoint
  --problem PROBLEM     Problem name: cvrp, vrptw, etc. or all
  --size SIZE           Problem size: 50, 100, for automatic loading
  --datasets DATASETS   Filename of the dataset(s) to evaluate. Defaults to all under data/{problem}/ dir
  --batch_size BATCH_SIZE
  --device DEVICE
  --remove-mixed-backhaul, --no-remove-mixed-backhaul
                        Remove mixed backhaul instances. Use --no-remove-mixed-backhaul to keep them. (default: True)

Citation

If you find RouteFinder valuable for your research or applied projects:

@article{
berto2025routefinder,
title={{RouteFinder: Towards Foundation Models for Vehicle Routing Problems}},
author={Federico Berto and Chuanbo Hua and Nayeli Gast Zepeda and Andr{\'e} Hottung and Niels Wouda and Leon Lan and Junyoung Park and Kevin Tierney and Jinkyoo Park},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=QzGLoaOPiY},
}