A genetic algorithm for a 2D industrial packing problem
Computers and Industrial Engineering
Computers and Industrial Engineering
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A New Exact Algorithm for General Orthogonal D-Dimensional Knapsack Problems
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
Tour Merging via Branch-Decomposition
INFORMS Journal on Computing
Encyclopedia of Optimization
Advances in Metaheuristics for Hard Optimization (Natural Computing Series)
Advances in Metaheuristics for Hard Optimization (Natural Computing Series)
Heuristic approaches for the two- and three-dimensional knapsack packing problem
Computers and Operations Research
An efficient computational procedure for determining the container-loading pattern
Computers and Industrial Engineering
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Combinations of local search and exact algorithms
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Applying backtracking heuristics for constrained two-dimensional guillotine cutting problems
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
The generate-and-solve framework revisited: generating by simulated annealing
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve cutting and packing problems, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. In this paper, we present an extended version of GS, focusing primarily on the concept of a new Density Control Operator (DCO). The role of this operator is to adaptively control the dimension of the reduced instances in such a way as to allow a much steadier progress towards a better solution, thereby avoiding premature convergence. In order to assess the potentials of this novel version of the GS methodology, we conducted computational experiments on a set of difficult benchmark instances of the constrained non-guillotine cutting problem. The results achieved are quantitatively and qualitatively discussed in terms of effectiveness and efficiency, showing that the proposed variant of the GS hybridization framework is highly suitable when effectiveness is a major requirement.