I have a client that runs a sports camp for kids. The kids get to request what sports they want to play, and what friends they want to be in class with. This creates a scheduling problem that's hard for a human, and previously they spent several man-weeks per year dealing with it. I built them a simple system that connects their data to an optimizer based on OR-Tools, now their scheduling is done with a few clicks.
yep, once you have the data, constraints, and utility functions properly* in the system you can brute force your way to many good enough solutions very quickly.
I coach a basketball league that has 8 periods. No player can play 2 more periods that any other player. The number of possible line-ups per game while still hitting the playing time contraint is astronomical. Very easy to find a series line-ups that fits the constraint, but very hard to find an optimal or near-optimal series of line-ups. It gets even more fun when you have to adjust for late arrivals or unannounced no-shows.
