Abstract
The optimization of two-dimensional guillotined cutting consists in determining a parts arrangement to be cut from a larger piece, maximizing the material use, but respecting the restrictions imposed by the cutting equipment and the production flow. An optimized cutting process maximizes the materials use and is an important factor for production systems performance at glassworks industries, impacting directly in the products final cost formation and, thus, increasing the company’s competitiveness in glass market. Several studies have shown that combinations of bio-inspired meta-heuristics, more specifically, the Genetic Algorithms (GA) and Ant Colony Optimization (ACO) are efficient techniques to solving constraint satisfaction problems and combinatorial optimization problems. GA and ACO are bio-inspired meta-heuristics techniques suitable for random guided solutions in problems with large search spaces. GA are search methods inspired by the natural evolution theory, presenting good results in global searches. ACO is based on the attraction of ants by pheromone trails while searching for food and uses a feedback system that enables rapid convergence in good solutions. The initial results from the combination of these two techniques when compared with the results each technique individually applied are encouraging and have presented interesting solutions to the problem of optimizing two-dimensional guillotined cutting.
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da Costa, F.M., Sassi, R.J. (2012). Application of an Hybrid Bio-inspired Meta-heuristic in the Optimization of Two-Dimensional Guillotine Cutting in an Glass Industry. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_95
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