[baybal2]

> solving distribution and flow is almost trivial. We have all sorts of systems for solving distribution problems and finding maximal flow along a network. There's even an entire class of algorithms to solve it with[1],

I would not be so confident with that. Yes, in a perfect world with perfectly rigid, cubical cows, and passengers that are never late you can make flow algo's that reliably hit near 'global maximums'. Things do not change dramatically if you feed the problem to some ML algo.

The flow problem for airlines is in bigger part a problem how to make decisions given near random occurrence of things like sudden worsening of the weather, traffic redirection due to something happening on the runway/airport, fueling trucks being late, and of course the need to maximize revenue per seat. The best decision making algorithm here would not be the one that maximizes throughput, but one that can keep routes more or less consistent given all those changes.

Here, I see that even if you had a near-perfect routing algorithm, and a transportation system with no human driven cars, the whole routing map may have to be changed completely in response minuscule externalities like somebody jaywalking. This class of problems is altogether different than any kind of optimization.

Ironically, the logic class most adapted for solving such problems is the long forgotten fuzzy logic.