RouteFinder: Towards Foundation Models for Vehicle Routing Problems
<|reference_start|>RouteFinder: Towards Foundation Models for Vehicle Routing Problems: 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 attribute combination. 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 24 VRP variants show RouteFinder achieves competitive results. Our code is openly available at https://github.com/ai4co/routefinder.<|reference_end|>
@article{berto2024routefinder:,
    title={RouteFinder: Towards Foundation Models for Vehicle Routing Problems},
    author={Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, Andr'e Hottung,
  Niels Wouda, Leon Lan, Junyoung Park, Kevin Tierney, Jinkyoo Park},
    journal={arXiv preprint arXiv:2406.15007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15007},
    primaryClass={cs.AI}
}