An Evolutionary Hyperheuristic to Solve Strip-Packing Problems

  • Pablo Garrido
  • María-Cristina Riff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


In this paper we introduce an evolutionary hyperheuristic approach to solve difficult strip packing problems. We have designed a genetic based hyperheuristic using the most recently proposed low-level heuristics in the literature. Two versions for tuning parameters have also been evaluated. The results obtained are very encouraging showing that our approach outperforms the single heuristics and others well-known techniques.


Hyperheuristic Strip Packing Evolutionary Algorithms 


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  1. 1.
    Alvarez, R., Parreño, F., Tamarit, J.M.: Reactive grasp for the strip packing problem. In: Proceedings of the 6th Metaheuristics International Conference, vol. 1 (2005)Google Scholar
  2. 2.
    Baker, B.S., Coffman, E.G., Rivest, R.L.: Orthogonal packings in two dimensions. SIAM Journal on Computing 9, 846–855 (1980)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Bortfeldt, A.: A genetic algorithm for the two-dimensional strip packing problem with rectangular pieces. European Journal of Operational Research 172, 814–837 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Bortfeldt, A., Gehring, H.: New large benchmark instances for the two-dimensional strip packing problem with rectangular pieces. In: IEEE Proceedings of the 39th Annual Hawaii International Conference on System Sciences, vol. 2, pp. 30–32 (2006)Google Scholar
  5. 5.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. Handbook of Metaheuristics 16, 457–474 (2003)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Burke, E., Kendall, G., Whitwell, G.: A new placement heuristic for the ortoghonal stock-cutting problem. Operations Research 52, 655–671 (2004)CrossRefGoogle Scholar
  7. 7.
    Chazelle, B.: The bottom-left bin packing heuristic: an efficient implementation. IEEE Transactions on Computers 32, 697–707 (1983)zbMATHCrossRefGoogle Scholar
  8. 8.
    Cowling, P., Kendall, G., Han, L.: An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of Congress on Evolutionary Computation, pp. 1185–1190 (2002)Google Scholar
  9. 9.
    Han, L., Kendall, G.: Guided operators for a hyper-heuristic genetic algorithm. In: Gedeon, T.D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 807–820. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Hopper, E.: Two-Dimensional Packing Utilising Evolutionary Algorithms and other Meta-Heuristic Methods. PhD. Thesis Cardiff University, UK (2000)Google Scholar
  11. 11.
    Hopper, E., Turton, B.C.H.: An empirical investigation on metaheuristic and heuristic algorithms for a 2d packing problem. European Journal of Operational Research 128, 34–57 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Iori, M., Martello, S., Monaci, M.: Metaheuristic algorithms for the strip packing problem, pp. 159–179. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  13. 13.
    Lesh, N., Marks, J., Mc Mahon, A., Mitzenmacher, M.: Exhaustive approaches to 2d rectangular perfect packings. Information Processing Letters 90, 7–14 (2004)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Lesh, N., Mitzenmacher, M.: Bubble search: A simple heuristic for improving priority-based greedy algorithms. Information Processing Letters 97, 161–169 (2006)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Martello, S., Monaci, M., Vigo, D.: An exact approach to the strip-packing problem. INFORMS Journal of Computing 15, 310–319 (2003)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Mumford-Valenzuela, C., Vick, J., Wang, P.Y.: Heuristics for large strip packing problems with guillotine patterns: An empirical study, pp. 501–522. Kluwer Academic Publishers, Dordrecht (2004)Google Scholar
  17. 17.
    Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: International Joint Conference on Artificial Intelligence, pp. 975–980 (2007)Google Scholar
  18. 18.
    Soke, A., Bingul, Z.: Hybrid genetic algorithm and simulated annealing for two-dimensional non-guillotine rectangular packing problems. Engineering Applications of Artificial Intelligence 19, 557–567 (2006)CrossRefGoogle Scholar
  19. 19.
    Zhang, D., Kang, Y., Deng, A.: A new heuristic recursive algorithm for the strip rectangular packing problem. Computers and Operations Research 33, 2209–2217 (2006)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pablo Garrido
    • 1
  • María-Cristina Riff
    • 1
  1. 1.Department of Computer Science, Universidad Técnica Federico Santa María, ValparaísoChile

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