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 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods. Code: https://github.com/ai4co/routefinder.

翻译：本文提出了RouteFinder，一个用于解决不同车辆路径问题变体的综合性基础模型框架。我们的核心思想是，VRP的基础模型应能够通过将每个变体视为配备不同属性的广义问题子集来表示它们。我们提出了一个统一的VRP环境，能够高效处理任意属性组合。RouteFinder模型利用基于现代Transformer的编码器和全局属性嵌入来改进任务表示。此外，我们引入了两种强化学习技术以提升多任务性能：混合批次训练（支持同时训练不同变体）和多变体奖励归一化（用于平衡不同奖励尺度）。最后，我们提出了高效的适配器层，能够针对具有未见属性的新变体进行微调。在48个VRP变体上的大量实验表明，RouteFinder优于当前最先进的学习方法。代码：https://github.com/ai4co/routefinder。