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+ + Vehicle Routing Problems (VRPs) are optimization problems with significant real-world implications + in logistics, transportation, and supply chain management. Despite the recent progress made in learning + to solve individual VRP variants, there is a lack of a unified approach that can effectively tackle a wide + range of tasks, which is crucial for real-world impact. This paper introduces RouteFinder, a framework for + developing foundation models for VRPs. Our key idea is that a foundation model for VRPs should be able to + model variants by treating each variant as a subset of a larger VRP problem, equipped with different attributes. + We introduce a parallelized environment that can handle any combination of attributes at the same time in a + batched manner, and an efficient sampling procedure to train on a mix of problems at each optimization step + that can greatly improve convergence robustness. We also introduce novel Global Feature Embeddings that + project instance-wise attributes efficiently onto the latent space and help the model understand different + VRP variants. Finally, we introduce Efficient Adapter Layers, a simple yet effective technique to finetune + pre-trained RouteFinder models to solve novel variants with previously unseen attributes outside of the + original feature space. We validate our approach through extensive experiments on 24 VRP variants, demonstrating + competitive results over recent multi-task learning models. We make our code openly available + at this https URL. +
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+ @article{berto2024routefinder,
+ title={RouteFinder: Towards foundation models for vehicle routing problems},
+ author={Berto, Federico and Hua, Chuanbo and Zepeda, Nayeli Gast and Hottung, Andr{\'e} and Wouda, Niels and Lan, Leon and Tierney, Kevin and Park, Jinkyoo},
+ journal={arXiv preprint arXiv:2406.15007},
+ year={2024}
+}