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Application of Bio-inspired Metaheuristics to Guillotined Cutting Processes Optimize in an Glass Industry

  • Flavio Moreira da Costa
  • Tiago Vieira Carvalho
  • Renato Jose Sassi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8028)

Abstract

Nowadays, sustainability is becoming a strong worry of our society. It can be defined as using resources to meet the needs of the present without compromising the ability of future generations to meet their own needs. An optimized cutting process minimizes the materials waste and is an important factor for production systems performance at glassworks industries, impacting directly in the products final cost formation and contributing for more environmentally sustainable products and production processes. 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 results from the combination of these two techniques, when compared with the results from usual processes, are encouraging and have presented interesting solutions to the problem of optimizing guillotined cutting processes.

Keywords

Genetic Algorithms Ant Colony Optimization Guillotined Cutting Glass Industry Sustainability 

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References

  1. 1.
    Blum, C., Ibáñez, M.L.: The Industrial Electronics Handbook: Intelligent systems, 2nd. CRC Press (2011)Google Scholar
  2. 2.
    Castro, L.N., Zuben, F.J.V.: Recent Developments in Biologically Inspired Computing. Idea Group Publishing (2005)Google Scholar
  3. 3.
    Costa, F.M., Canto, N., Sassi, R.J.: Study of the Application of Genetic Algorithms in Optimization of Cutting Glass Sheets. In: Proceedings of the 9th IEEE/IAS International Conference on Industry Applications, Industry Applications (INDUSCON) 9th IEEE/IAS International Conference, São Paulo, vol. 1, pp. 1–3 (2010)Google Scholar
  4. 4.
    da Costa, F.M., Sassi, R.J.: 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.) IDEAL 2012. LNCS, vol. 7435, pp. 802–809. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Cuthbertson, R., Piotrowicz, W.: Supply chain best practices – identification and categorisation of measures and benefits. International Journal of Productivity & Performance Management 57(5), 389–404 (2008)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Book (2004)Google Scholar
  7. 7.
    Grosan, C., Abraham, A.: Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms. SCI, vol. 75, pp. 1–17. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  9. 9.
    Hoseini, P., Shayesteh, M.G.: Hybrid Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing for Image Contrast Enhancement. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6 (2010)Google Scholar
  10. 10.
    Linton, J.D., Klassen, R., Jayaraman, V.: Sustainable supply chains: An introduction. Journal of Operations Management 25, 1075–1082 (2007)CrossRefGoogle Scholar
  11. 11.
    Temponi, E.C.: Uma Proposta de Resolução do Problema de Corte Bidimensional via Abordagem Metaheurística, Dissertação de Mestrado. Diretoria de Pesquisa e Pós-Graduação, CEFET-MG (2007)Google Scholar
  12. 12.
    Zhang, D., Du, L.: Hybrid Ant Colony Optimization Based on Genetic Algorithm for Container Loading Problem. In: International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 10–14 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Flavio Moreira da Costa
    • 1
  • Tiago Vieira Carvalho
    • 1
  • Renato Jose Sassi
    • 1
  1. 1.Industrial Engineering Postgraduation ProgramNove de Julho University – UNINOVESão PauloBrasil

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