[beret4breakfast]

It feels like there’s a significant lack of “ML/AI” based approaches applied to these kinds of problems. I’ve seen a lot of example of RL/GNN papers that do attempt to solve smaller problems but it always feels like the best option is to just pay for a gurobi license and have at it. I’ve been doing some scheduling optimisation recently (close to job shop scheduling) and while there’s some examples of using RL they just don’t seem to cut it. I’ve resorted to evolutionary algorithms to get reasonable solutions to some big problems. Maybe it’s just always more efficient to using OR type approaches when you can formulate the problem well.

[7thaccount]

It depends on the problem. The security contained unit commitment problem (how you figure out which power plants to turn on when) is an unbelievably complex problem that MILP solvers like Gurobi can find globally optimal solutions (within the bounds of the MIP gap) quickly. Sure you could create a genetic algorithm, but there is no guarantee it will give you an answer that isn't stuck in a local minima. That is assuming you can make it run fast. Neural networks are also going to be sub optimal.

[zozbot234]

SAT is a standard GOFAI problem and you can of course use any programming language in the ML family to write a SAT solver. Thus I'd say that "ML/AI" approaches are, if anything, quite applicable!