What's the current state/consensus on using neural networks for solving combinatorial scheduling problems?

Historically, the most practical methods for solving real-world combinatorial scheduling problems have been using heuristics or metaheurisics such as simulated annealing, tabu search, greedy randomized adaptive search, etc... I consider these more operation research-based techniques.

However, recently we have obviously seen a lot of progress being made in the machine learning realm for many types of problems. In particular, we've seen neural networks be used to train models based on data in text, audio, or video form.

I am wondering …

consensus current etc heuristics networks neural networks neuralnetworks practical progress research scheduling search state world