[ant6n]

For my public transit planning course, I tried to use gurobi to optimize the schedule of a given fictional city involving a single inner city loop with 8 branches or so. The problem ended up being an MILP with tens or hundreds of thousands of variables, and gurobi couldn’t give me optimal solutions after days of trying - only approximations, with poor bounds.

I know my approach may have been naive, but so was the problem. I think in real life people tend to use some sort of stochastic search to get good solutions, perhaps genetic algorithms or simulated annealing. Which won’t give u provably optimal solutions, but you’ll more quickly get good solutions.

Gurobi is my go to method for optimization, but I often don’t get solutions.

[sirwhinesalot]

There's an interesting tool called LocalSolver that uses local search methods by default for everything but if it detects a "decidable" class of problem it can also use fancier algorithms capable of returning a global optima, likely running both in parallel.

Sadly I haven't had a chance to try it.