Resource Constrained Job Scheduling with Parallel Constraint-Based ACO

Hybrid methods are highly effective means of solving combinatorial optimization problems and have become increasingly popular. In particular, integrations of exact and incomplete methods have proved to be effective where the hybrid takes advantage of the relative performance of each individual method. However, these methods often require significant run-times to determine good feasible solutions. One way of reducing run-times is to parallelize these algorithms. For large NP-hard problems, parallelization must be done with care, since changes to the algorithm can affect its performance in unpredictable ways. In this paper we develop two parallel variants of constraint-based ACO and test them on a problem arising in the Australian mining industry. We demonstrate that parallelization significantly reduces run times with each parallel variant providing advantages with respect to feasibility and solution quality.