Recently, I heard from someone (that I was trying to convince how CP is cool) : « yes but you know, most of the time we have an objective function … ». This person has a very strong MIP background and it’s not the first time I heard such a belief. Needless to say this is wrong and no, CP is not only for satisfaction problems.
CP usually solves optimization problems with B&B by optimizing one variable having its own domain. In short, this is how it works:
When a solution is found, a constraint is dynamically added (without restarting the search) such that the next solution found is strictly better. The last solution found is by construction optimal (correct me if I’m wrong but I think this adaptation of B&B to CP was imagined by Pascal Van Hentenryck).
This is also completely false to say that CP doesn’t use lower bounds. Usually the objective function/variable is linked into one (or several) global constraint computing very good lower bounds on it (look for instance the paper of Jean-Charles: « Arc consistency for global cardinality constraints with costs ». With this kind of constraints you can solve TSP problems with CP. This is a small demo on google map I made some time ago http://travellingsalesmanproblem.appspot.com/ It is a bit slow due to the time needed to collect the distance matrix L