Summary of Routefinder: Towards Foundation Models For Vehicle Routing Problems, by Federico Berto et al.
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
by Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, André Hottung, Niels Wouda, Leon Lan, Junyoung Park, Kevin Tierney, Jinkyoo Park
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces RouteFinder, a comprehensive framework for tackling different variants of the Vehicle Routing Problem (VRP). The core idea is that a foundation model should be able to represent VRP variants by treating each as a subset of a generalized problem equipped with different attributes. The proposed unified VRP environment can efficiently handle any attribute combination using a modern transformer-based encoder and global attribute embeddings. Two reinforcement learning techniques are introduced: mixed batch training for training on different variants at once, and multi-variant reward normalization to balance different reward scales. Efficient adapter layers 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RouteFinder is a new way to solve complex transportation problems. Imagine you need to deliver packages to many different places, and you want to find the most efficient route. This paper creates a special kind of computer program that can help with this problem. It’s called RouteFinder, and it can solve lots of different versions of this problem. The program uses a new type of computer model called a “transformer” to understand the problems. It also has special tools that let it work well on many different types of transportation problems. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Reinforcement learning » Transformer
