Learning the Quality of Machine Permutations in Job Shop Scheduling. (arXiv:2207.03244v2 [cs.LG] UPDATED)

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 algorithms.
One combinatorial optimization problem recently tackled with ML is the Job Shop
scheduling Problem (JSP). Most of the works on the JSP using ML focus on Deep
Reinforcement Learning (DRL), and only a few of them leverage supervised
learning techniques. The recurrent reasons for avoiding supervised learning …

arxiv job machine permutations quality scheduling