Combinations of local search and exact algorithms
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Computers and Industrial Engineering
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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This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of Integer Linear Programming (ILP) and Genetic Algorithms (GAs). More precisely, a GA engine works as a generator of reduced instances for the original CL problem, which are formulated as ILP models. These instances, in turn, are solved by an exact optimization technique (solver), and the performance measures accomplished by the respective models are interpreted as fitness values by the genetic algorithm, thus guiding its evolutionary process. Computational experiments performed on standard benchmark problems, as well as a practical case study developed in a metallurgic factory, are also reported and discussed here in a manner as to testify the potentialities behind the novel approach.