Summary of Leveraging Constraint Programming in a Deep Learning Approach For Dynamically Solving the Flexible Job-shop Scheduling Problem, by Imanol Echeverria et al.
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
by Imanol Echeverria, Maialen Murua, Roberto Santana
First submitted to arxiv on: 14 Mar 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 This paper proposes a novel methodology that combines constraint programming (CP) and deep learning (DL) to solve the flexible job-shop scheduling problem (FJSSP). By training a DL model using optimal solutions generated by CP, the approach learns from high-quality data, eliminating the need for extensive exploration. The hybrid method integrates CP into the DL framework, utilizing DL for complex stages and transitioning to CP for optimal resolution as the problem simplifies. Experimental results on three public FJSSP benchmarks show superior performance compared to five state-of-the-art DRL approaches and a widely-used CP solver. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes two big steps forward in solving scheduling problems! It combines two powerful techniques: constraint programming (CP) and deep learning (DL). By teaching the DL model to find good solutions using CP, it can learn really fast without trying many different options. Then, by switching from DL to CP when things get simpler, it makes sure we get the best answer possible. The results show that this new way of solving problems is better than others at getting the right answers for certain types of scheduling challenges. |
Keywords
» Artificial intelligence » Deep learning
