A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

We propose a metaheuristic algorithm for the multi-resource generalizedassignment problem (MRGAP). MRGAP is a generalization of the generalized assignmentproblem, which is one of the representative combinatorial optimization problems knownto be NP-hard. The algorithm features a very large-scale neighborhood search, which is amechanism of conducting the search with complex and powerful moves, where the resultingneighborhood is efficiently searched via the improvement graph. We also incorporate anadaptive mechanism for adjusting search parameters, to maintain a balance between visitsto feasible and infeasible regions. Computational comparisons on benchmark instancesshow that the method is effective, especially for type D and E instances, which are knownto be quite difficult.