Summary of Hierarchical Neural Constructive Solver For Real-world Tsp Scenarios, by Yong Liang Goh et al.
Hierarchical Neural Constructive Solver for Real-world TSP Scenarios
by Yong Liang Goh, Zhiguang Cao, Yining Ma, Yanfei Dong, Mohammed Haroon Dupty, Wee Sun Lee
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 realistic Traveling Salesman Problem (TSP) scenarios for industrial settings and proposes a hierarchical approach to solve TSP by prioritizing choices based on current locations. The authors integrate a learnable choice layer inspired by Hypernetworks and a learnable approximate clustering algorithm inspired by the Expectation-Maximization algorithm. The proposed approach outperforms both classical and recent transformer models, demonstrating its effectiveness in solving realistic TSP instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the shortest route that visits certain cities and then returns to the starting point. It’s like planning a trip for a salesman who has to visit many places and then come back home. The problem is important because it helps us understand how to solve other problems that involve finding the best way to do something. |
Keywords
* Artificial intelligence * Clustering * Transformer
