Learning to generalize Dispatching rules on the Job Shop Scheduling. (arXiv:2206.04423v2 [cs.LG] UPDATED)

This paper introduces a Reinforcement Learning approach to better generalize
heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current
models on the JSP do not focus on generalization, although, as we show in this
work, this is key to learning better heuristics on the problem. A well-known
technique to improve generalization is to learn on increasingly complex
instances using Curriculum Learning (CL). However, as many works in the
literature indicate, this technique might suffer from catastrophic forgetting
when transferring …

arxiv job rules scheduling