Learning the Quality of Machine Permutations in Job Shop Scheduling. (arXiv:2207.03244v1 [cs.LG])

In recent years, the power demonstrated by Machine Learning (ML) has
increasingly attracted the interest of the optimization community that is
starting to leverage ML for enhancing and automating the design of optimal and
approximate algorithms. One combinatorial optimization problem that has been
tackled with ML is the Job Shop scheduling Problem (JSP). Most of the recent
works focusing on the JSP and ML are based on Deep Reinforcement Learning
(DRL), and only a few of them leverage supervised learning …

arxiv job learning lg machine permutations quality scheduling