Improving CP-based local branching via sliced neighborhood search.

In this paper we merge two problem independent search strategies, namely Local Branching and Sliced Neighborhood Search. They both integrate CP tree search with local search concepts, but while local branching is very effective in exploring small neighborhoods, its performances decrease when dealing with diversification and large neighborhood exploration. On the other hand Sliced Neighborhood Search is an effective method for exploring random slices of large neighborhoods and in moving away arbitrarily far from an incumbent solution. For this reason we obtain very good results in improving a reference solution: Local Branching obtains a 35% improvement when SNS is integrated aggressively both in the neighborhood exploration and in the diversification strategy. The tests were conducted on large instances of the Travelling Salesman Problem with Time Windows.