Summary of Graph Neural Networks For Job Shop Scheduling Problems: a Survey, by Igor G. Smit et al.
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
by Igor G. Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu, Jian Chen, Cong Zhang, Zaharah Bukhsh, Yingqian Zhang, Wim Nuijten
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper provides a comprehensive review of graph neural networks (GNNs) applied to job shop scheduling problems (JSSPs) and flow-shop scheduling problems (FSPs). It highlights the recent surge in using GNNs for solving JSSPs, despite lacking a systematic survey of the relevant literature. The authors present various graph representations of JSSPs and introduce commonly used GNN architectures. They then review current GNN-based methods for each problem type, including key technical elements such as graph representations, GNN architectures, tasks, and training algorithms. The paper analyzes the advantages and limitations of GNNs in solving JSSPs and identifies potential future research opportunities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to use special kinds of artificial intelligence called graph neural networks (GNNs) to solve tricky scheduling problems. Scheduling is like planning a day or a week – you have tasks, resources, and time constraints. GNNs are great for solving these kinds of problems because they can learn from examples and make good decisions. The paper shows how different GNN approaches work together with deep learning and reinforcement learning to solve job shop scheduling problems (where machines have different speeds) and flow-shop scheduling problems (where machines process tasks in a specific order). It also looks at what works well and what doesn’t, giving ideas for future research. |
Keywords
* Artificial intelligence * Deep learning * Gnn * Reinforcement learning
