Summary of Cross-problem Learning For Solving Vehicle Routing Problems, by Zhuoyi Lin et al.
Cross-Problem Learning for Solving Vehicle Routing Problems
by Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu
First submitted to arxiv on: 17 Apr 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 proposed cross-problem learning approach helps train neural heuristics for various vehicle routing problem (VRP) variants by leveraging transferable knowledge. A modularized architecture is designed, consisting of a backbone Transformer for tackling the travelling salesman problem (TSP), and additional lightweight modules processing problem-specific features in complex VRPs. The approach involves pre-training the backbone Transformer for TSP, followed by fine-tuning for each target VRP variant using either full or adapter-based fine-tuning methods. Experimental results demonstrate significant performance improvements over training from scratch, as well as comparable performance with reduced parameters. The method also shows promise for cross-distribution applications and versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps neural heuristics learn to solve different types of vehicle routing problems by sharing knowledge between them. It creates a special architecture that can be used across many types of problems. This architecture is made up of two parts: one part, called the backbone Transformer, is designed to solve the traveling salesman problem, and the other part is custom-made for each specific problem. The researchers tested their method on several types of vehicle routing problems and found that it works much better than starting from scratch. They also showed that the method can be used across different datasets and works well. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
