The integration of tree search and metaheuristic techniques for the solution of combinatorial optimization
problems is a research area widely explored in the last decade. We propose a search strategy called
Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent
solution by performing constraint programming tree search (along with constraint propagation). SNS
encloses concepts from metaheuristic techniques. SNS can be used both as a stand alone search strategy,
and embedded in other strategies as intensification and diversification mechanism. We provide an extensive
experimental evaluation of SNS on hard instances of the Asymmetric Traveling Salesman Problem
with Time Windows. We show the effectiveness of SNS as a stand alone strategy when compared to
Limited Discrepancy Search and of SNS when included in a heuristic framework such as the constraint
programming based local branching.