Summary of An Efficient Learning-based Solver Comparable to Metaheuristics For the Capacitated Arc Routing Problem, by Runze Guo et al.
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem
by Runze Guo, Feng Xue, Anlong Ming, Nicu Sebe
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 Recently, neural networks (NN) have made significant advancements in combinatorial optimization, but they struggle when tackling the capacitated arc routing problem (CARP), which involves finding the minimum-cost tour covering all required edges on a graph while respecting capacity constraints. To address this challenge, we introduce an NN-based solver that narrows the gap with advanced metaheuristics and exhibits superior efficiency. Our approach combines direction-aware attention modeling to incorporate directionality into the embedding process and a supervised reinforcement learning scheme for effective one-stage decision-making. We also propose a path optimization method to adjust depot return positions within the path generated by our DaAM model. Experimental results show that our approach outperforms heuristics and achieves decision quality comparable to state-of-the-art metaheuristics for CARP, while maintaining superior efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a problem called capacitated arc routing (CARP) where you need to find the best route on a map that covers all the required places. Neural networks are good at solving some problems, but they struggle with CARP because they don’t understand direction well. The researchers developed a new way to use neural networks for CARP that is better than previous methods and takes less time. They used two main ideas: first, they figured out how to make the neural network understand direction; second, they trained the neural network to make good decisions. The results show that this new method works well and can solve CARP problems more efficiently. |
Keywords
* Artificial intelligence * Attention * Embedding * Neural network * Optimization * Reinforcement learning * Supervised
