Datasets:
license: mit
size_categories:
- 10K<n<100K
task_categories:
- reinforcement-learning
- graph-ml
tags:
- NCO
- AI4CO
- RL4CO
- vrp
- vehicle-routing-problems
RouteFinder Data
This repository contains datasets used and generated by RouteFinder, a comprehensive foundation model framework to tackle various Vehicle Routing Problem (VRP) variants.
Paper: RouteFinder: Towards Foundation Models for Vehicle Routing Problems GitHub Repository: https://github.com/ai4co/routefinder
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.
Sample Usage
To get started with RouteFinder, you can download the data and checkpoints, and then run an experiment using the provided scripts.
Download data and checkpoints
To download the data and checkpoints from Hugging Face automatically, you can use:
python scripts/download_hf.py
Running an experiment
You can run a main experiment, for example, with the RouteFinder Transformer model:
python run.py experiment=main/rf/rf-transformer-100
For more detailed instructions, configuration options, and other scripts (e.g., for testing or data generation), please refer to the GitHub repository.
Available Environments
The dataset provides instances for 48 VRP variants. All variants include the base Capacity (C). The five features Open Route (O), Backhaul (B), Duration Limit (L), Time Windows (TW), and Multi-depot (MD) can be combined into any subset, creating $2^5 = 32$ possible combinations. The Mixed (M) global feature adds 16 more Mixed Backhaul (MB) variants, resulting in a total of 48 variants.
| VRP Variant | Capacity (C) | Open Route (O) | Backhaul (B) | Mixed (M) | Duration Limit (L) | Time Windows (TW) | Multi-depot (MD) |
|---|---|---|---|---|---|---|---|
| CVRP | ✔ | ||||||
| OVRP | ✔ | ✔ | |||||
| VRPB | ✔ | ✔ | |||||
| VRPL | ✔ | ✔ | |||||
| VRPTW | ✔ | ✔ | |||||
| OVRPTW | ✔ | ✔ | ✔ | ||||
| OVRPB | ✔ | ✔ | ✔ | ||||
| OVRPL | ✔ | ✔ | ✔ | ||||
| VRPBL | ✔ | ✔ | ✔ | ||||
| VRPBTW | ✔ | ✔ | ✔ | ||||
| VRPLTW | ✔ | ✔ | ✔ | ||||
| OVRPBL | ✔ | ✔ | ✔ | ✔ | |||
| OVRPBTW | ✔ | ✔ | ✔ | ✔ | |||
| OVRPLTW | ✔ | ✔ | ✔ | ✔ | |||
| VRPBLTW | ✔ | ✔ | ✔ | ✔ | |||
| OVRPBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| VRPMB | ✔ | ✔ | ✔ | ||||
| OVRPMB | ✔ | ✔ | ✔ | ✔ | |||
| VRPMBL | ✔ | ✔ | ✔ | ✔ | |||
| VRPMBTW | ✔ | ✔ | ✔ | ✔ | |||
| OVRPMBL | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| OVRPMBTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| VRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| OVRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| MDCVRP | ✔ | ✔ | |||||
| MDOVRP | ✔ | ✔ | ✔ | ||||
| MDVRPB | ✔ | ✔ | ✔ | ||||
| MDVRPL | ✔ | ✔ | ✔ | ||||
| MDVRPTW | ✔ | ✔ | ✔ | ||||
| MDOVRPTW | ✔ | ✔ | ✔ | ✔ | |||
| MDOVRPB | ✔ | ✔ | ✔ | ✔ | |||
| MDOVRPL | ✔ | ✔ | ✔ | ✔ | |||
| MDVRPBL | ✔ | ✔ | ✔ | ✔ | |||
| MDVRPBTW | ✔ | ✔ | ✔ | ✔ | |||
| MDVRPLTW | ✔ | ✔ | ✔ | ✔ | |||
| MDOVRPBL | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDOVRPBTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDOVRPLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDVRPBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDOVRPBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| MDVRPMB | ✔ | ✔ | ✔ | ✔ | |||
| MDOVRPMB | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDVRPMBL | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDVRPMBTW | ✔ | ✔ | ✔ | ✔ | ✔ | ||
| MDOVRPMBL | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| MDOVRPMBTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| MDVRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| MDOVRPMBLTW | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Citation
If you find RouteFinder valuable for your research or applied projects, please cite using the following BibTeX:
@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},
}