An Algorithm for the Generalized Assignment Problem with Special Ordered Sets
Journal of Heuristics
An LP-based heuristic procedure for the generalized assignment problem with special ordered sets
Computers and Operations Research
Multiobjective Landscape Analysis and the Generalized Assignment Problem
Learning and Intelligent Optimization
On solving the Lagrangian dual of integer programs via an incremental approach
Computational Optimization and Applications
Plateau connection structure and multiobjective metaheuristic performance
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution algorithms for the generalized assignment problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A computational study of exact knapsack separation for the generalized assignment problem
Computational Optimization and Applications
A path relinking approach for the multi-resource generalized quadratic assignment problem
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
DNA sequence design by dynamic neighborhood searches
DNA'06 Proceedings of the 12th international conference on DNA Computing
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
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We propose a tabu search algorithm for the generalized assignment problem, which is one of the representative combinatorial optimization problems known to be NP-hard. The algorithm features an ejection chain approach, which is embedded in a neighborhood construction to create more complex and powerful moves. We also incorporate an adaptive mechanism for adjusting search parameters, to maintain a balance between visits to feasible and infeasible regions. Computational results on benchmark instances of small sizes show that the method obtains solutions that are optimal or that deviate by at most 0.16% from the best known solutions. Comparisons with other approaches from the literature show that, for instances of larger sizes, our method obtains the best solutions among all heuristics tested.