A Synchronized Sweep Algorithm for the k-dimensional cumulative Constraint

This paper presents a sweep based algorithm for the kdimensional cumulative constraint, which can operate in filtering mode as well as in greedy assignment mode. Given n tasks and k resources, this algorithm has a worst-case time complexity of O(kn) but scales well in practice. In greedy assignment mode, it handles up to 1 million tasks with 64 resources in one single constraint in SICStus. In filtering mode, on our benchmarks, it yields a speed-up of about k when compared to its decomposition into k independent cumulative constraints.