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 branch

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.