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

Authors:
Mutsunori Yagiura;Shinji Iwasaki;Toshihide Ibaraki;Fred Glover
Affiliations:
Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan;Web Service Group, NTT Data Corporation, Kayabacho Tower Bldg., 21-2, Shinkawa 1-Chome, Chuo-Ku, Tokyo 104-0033, Japan;Department of Mathematics, School of Science and Technology, Kwanse, Gakuen University, 2-1 Gakuen, Sanda 669-1337, Japan;Leeds School of Business, University of Colorado, Boulder, CO 80309-0419, USA
Venue:
Discrete Optimization
Year:
2004

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Abstract

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