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Enhancing Accuracy of Hybrid Packing Systems through General-Purpose Characterization

  • Laura Cruz-Reyes
  • Claudia Gómez-Santillán
  • Satu Elisa Schaeffer
  • Marcela Quiroz-Castellanos
  • Victor M. Alvarez-Hernández
  • Verónica Pérez-Rosas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)

Abstract

Some Hybrid Packing Systems integrate several algorithms to solve the bin packing problem (BPP) based on their past performance and the problem characterization. These systems relate BPP characteristics with the performance of the set of solution algorithms and allow us to estimate which algorithm is to yield the best performance for a previously unseen instance. The present paper focuses on the characterization of NP-hard problems. In related work, characterization metrics are traditionally oriented towards problem structure. In this work, we propose metrics based on descriptive statistics for the Bin Packing Problem (BPP). The proposed metrics are of general purpose, meaning that the metrics do not depend on problem structure and can be applied to BPP and other problems to complement existent metrics. The “enhanced” Hybrid Packing System outperforms the version that does not take advantage of the general-purpose metrics; the results obtained show a 3%-improvement with respect to the reference Packing System.

Keywords

Hybrid Solution Systems Bin Packing Problem Problem Characterization Heuristic Algorithms Algorithm Selection 

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References

  1. 1.
    Papadimitriou, C., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Mineola. Dover Publications, New York (1998)zbMATHGoogle Scholar
  2. 2.
    Reeves, C.R.: Modern heuristic techniques for combinatorial problems. John Wiley & Sons, New York (1993)zbMATHGoogle Scholar
  3. 3.
    Smith-Miles, K., James, R., Giffin, J., Tu, Y.: Understanding the relationship between scheduling problem structure and heuristic performance using knowledge discovery. In: Learning and Intelligent Optimization, LION, vol. 3 (2009)Google Scholar
  4. 4.
    Messelis, T., Haspeslagh, S., Bilgin, B., De Causmaecker, P., Vanden Berghe, G.: Towards prediction of algorithm performance in real world optimisation problems. In: Proceedings of the 21st Benelux Conference on Artificial Intelligence, BNAIC, Eindhoven pp. 177–183 (2009)Google Scholar
  5. 5.
    Pérez, J., Pazos, R.A., Frausto, J., Rodríguez, G., Romero, D., Cruz, L.: A Statistical Approach for Algorithm Selection. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 417–431. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Borghetti, B.J.: Inference algorithm Performance and Selection under Constrained Resources. M.Sc. Thesis. Faculty of the School of Engineering Air University Force Institute of Technology Air University (1996)Google Scholar
  7. 7.
    Yuan, B.: Towards Improved Experimental Evaluation and Comparison of Evolutionary Algorithms. Ph.D. Thesis, School of Information Technology and Electrical Engineering of The University of Queensland, Australia (2006)Google Scholar
  8. 8.
    Gagliolo, M., Schmidhuber, J.: Learning Dynamic Algorithm Portfolios. In: AI & MATH 2006, Special Issue of the Annals of Mathematics and Artificial Intelligence, vol. 47, pp. 295–328. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the Empirical Hardness of Optimization Problems: the case of combinatorial auctions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 556–572. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Coffman, E.G., Courboubetis, C., Garey, M.R., Johnson, D.S., Shor, P.W., Weber, R.R.: Perfect Packing Theorems and the Average Case Behavior of Optimal and Online Bin Packing. ACM-SIAM Review. Society for Industrial and Applied Mathematics 44, 95–108 (2002)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Coffman, J., Garey, M., Jonson, D.: Approximation Algorithms for Bin-Packing, a Survey. In: Approximation Algorithms for NP-hard Problems, pp. 46–93. PWS, Boston (1997)Google Scholar
  12. 12.
    Coffman, J.E., Galambos, G., Martello, S., Vigo, D.: Bin Packing Approximation Algorithms: Combinatorial Analysis. In: Du, D.-Z., Pardalos, P.M. (eds.) Handbook of Combinatorial Optimization, pp. 151–207. Kluwer Academic Publishers, Dordrecht (1999)CrossRefGoogle Scholar
  13. 13.
    Pérez, J., Pazos, R.A., Vélez, L., Rodríguez, G.: Automatic Generation of Control Parameters for the Threshold Accepting Algorithm. In: Coello, C.A., Albornoz, A., Sucar, L.E., Cairó, O.B. (eds.) LNCS, vol. 4128, pp. 119–127. Springer, Heidelberg (2002)Google Scholar
  14. 14.
    Ducatelle, F., Levine, J.: Ant Colony Optimisation for Bin Packing and Cutting Stock Problems. In: Proceedings of the UK Workshop on Computational Intelligence, Edinburgh (2001)Google Scholar
  15. 15.
    Ross, S.M.: Simulación. Segunda edición. Prentice Hall, EEUU (1999)Google Scholar
  16. 16.
    Weisstein, E.W.: CRC Concise Encyclopedia of Mathematics, 2nd edn. CRC Press, FL (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning Multi-label Scene Classification. Pattern Recognition 37, 1757–1771 (2004)CrossRefGoogle Scholar
  18. 18.
    Wieczorkowska, A., Synak, P., Ras, Z.: Multi-label classification of emotions in music. In: Proceedings of IIS 2006 Symposium, Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 307–315. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Alsabti, K., Ranka, S., Singh, V.: An Efficient K-Means Clustering Algorithm. In: IPPS/SPDP Workshop on High Performance Data Mining, Orlando, Florida (1998)Google Scholar
  20. 20.
    Beasley, J.E.: Bin Packing - one dimensional. Brunel University. The Beasley’s OR-Library, http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html
  21. 21.
    Scholl, A., Klein, R.: Bin Packing, Jena Univerity, http://www.wiwi.uni-jena.de/Entscheidung/binpp/metric.htm
  22. 22.
    Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo; Book Review, vol. 16(3), pp. 235–240. Springer, Heidelberg (1993)Google Scholar
  23. 23.
    Dallas, J.: Métodos Multivariados Aplicados al Análisis de Datos. In: Thomson (ed.) Internacional, México (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Laura Cruz-Reyes
    • 1
  • Claudia Gómez-Santillán
    • 1
  • Satu Elisa Schaeffer
    • 2
  • Marcela Quiroz-Castellanos
    • 1
  • Victor M. Alvarez-Hernández
    • 1
  • Verónica Pérez-Rosas
    • 1
  1. 1.Instituto Tecnológico de Ciudad MaderoITCMMexico
  2. 2.Facultad de Ingeniería Mecánica y EléctricaUANLMexico

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