RouteFinder: Towards Foundation Models for Vehicle Routing Problems

• URL: http://arxiv.org/abs/2406.15007v2
• Date: Wed, 09 Oct 2024 14:23:24 GMT
• Title: RouteFinder: Towards Foundation Models for Vehicle Routing Problems
• Authors: Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, André Hottung, Niels Wouda, Leon Lan, Junyoung Park, Kevin Tierney, Jinkyoo Park,
• Abstract summary: RouteFinder is a 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.
• Score: 21.3310292139361
• License:
• 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 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.

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