[botexpert]

It is, but saying it's machine learning hides the combinatorial issues and combinatorial search that has to happen. Machine learning model can't replace the necessity for search.

Scheduling problem (the general one) maps easily to the vehicle routing problem. Vehicles are routed and service customers. On-demand requirements and scheduling are probably easier to conceptualize in that framework (think Uber but with ride-sharing bus sized cars, or team picking up and delivering food from restaurants to locations, or repairmen doing stuff at people homes).

All needs combinatorial search and machine learning is only one little piece of the puzzle.

[xapata]

I usually say it the other way -- optimization is just one part of machine learning.

ML includes optimization/search, dimensionality reduction (aka unsupervised learning), prediction (aka supervised learning), and reinforcement learning. And I'm probably forgetting a category.

[botexpert]

ML does not include integer linear programming (nothing learnable there) and similar mathematical optimizations. ML can be a part of it but is not integral to it. Just like finding the shortest paths between two points does not include ML. ML can be used to learn the weights through time (traffic sensitive routing) but the search part is separated from it.

IMO, ML is nowhere close to useful in these problems when most of the clients just use pen and paper. It should be fairly easy to beat that with some simple search heuristics.

[xapata]

Simple is better than complex. I agree with that.