I talk briefly about this in my video on Heuristic Functions (I won't link just to stop shameless plugging), but its a combination of funding, knowledge, and team.

Scheduling problems are a specific type of Optimization problem and many of those problems are NP-hard to exponential in complexity. For example a 25x25 Linear Assignment Problem has 25! potentially configurations, which would take longer than the heat death of the universe to find the global maxima. It doesn't matter what algorithm you use, finding the absolute best solution and proving it is impossible.

I'm not super familiar with Hungarian/Munkres but a quick GPT conversation points out that its O(n^3) isn't really good for those types of large problem sizes. Even if 25! isn't bad, I can always increase my N.

Again, how do you decide if Hungarian is better than Simulated? You find a bunch of problems that both can do and test the algorithms against them. If you get decent enough results, I'm sure there's a science journal out there that would also publish the results.