The search for the 'exactly optimal solution' is way overrated
I think you can get a moderately efficient solution using heuristics at 1/10 of the time or less
Not to mention developer time and trying to figure out which constraints make your problem infeasible. Especially as they get more complicated because you want to make everything linear
I agree, especially when considering that a model is also not reality.
However, what folks often do is find a Linear Solution quickly, then optimize on the Integer Solution, which gives you a gap that you can use to choose termination.
The vast majority of the United States power grid (many thousands of power plants) are optimized in auctions every hour for the next day and every 5 minutes on the operating day. Finding the globally optimal solution is pretty important for both fairness and not wasting billions of dollars each year. I'd agree with you for a lot of problems though, but keep in mind there are plenty where they need full optimality or within a tiny percentage from it.
The search for the 'exactly optimal solution' is way overrated
I think you can get a moderately efficient solution using heuristics at 1/10 of the time or less
Not to mention developer time and trying to figure out which constraints make your problem infeasible. Especially as they get more complicated because you want to make everything linear