We’ve been talking about various types of scheduling problems in my AI class, so this local article about computer modeling used to schedule sports games caught my eye. It is an interesting constraint problem – not just the number of games, mix of who plays who, and frequency of games, but particular rules based on amount of time needed to set aside for travel and other issues of fairness. It is particularly worthwhile to think about the advantages this system offers when changes occur that make a planned upon schedule no longer acceptable. Often, the human response to that is to try to find the solution that requires the fewest shifts possible, in part because it avoids “messing up” large parts of the already-difficult-to-construct schedule. With this type of software in place, it becomes debatable whether the fix with the fewest changes is optimal compared to the fix that results in a new global optimization. This is probably a place where knowing a bit more about sports would help me.