OR has essentially been customized to this domain long before generalized ML appeared on the scene - I can't imagine some of-the-self sci-kit learn libraries have improved on this much.
I work in OR, specifically Computational Logistics.
There is plenty of room for operational improvement in all industry, all the time. Articles like this paint us as big and stupid because that's how the "disruption" people see all industry.
Often it is not the optimization that is lacking but the will of disparate companies to co-operate.
An example - the Port of Rotterdam barge system is horribly inefficient for the barge operators because the individual shippers see minor gains. The challenge is getting the shippers to agree and co-operate, not to machine learn the best routing for the barges.
I think you are thinking of linear programming, and OR is way more than that. OR is the first chapter of an OR book.
(however I think the MIP people waste too much time looking for the perfect linear model and the best solution instead of using things like simulated annealing which might give a very good solution in 10% of the time)