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Ant colony optimization and local search for bin packing and cutting stock problems

Author

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  • J Levine

    (University of Edinburgh)

  • F Ducatelle

    (University of Edinburgh)

Abstract

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.

Suggested Citation

  • J Levine & F Ducatelle, 2004. "Ant colony optimization and local search for bin packing and cutting stock problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 705-716, July.
  • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:7:d:10.1057_palgrave.jors.2601771
    DOI: 10.1057/palgrave.jors.2601771
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    References listed on IDEAS

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    Cited by:

    1. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    2. K A Dowsland & J M Thompson, 2005. "Ant colony optimization for the examination scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 426-438, April.
    3. Hawa, Asyl L. & Lewis, Rhyd & Thompson, Jonathan M., 2022. "Exact and approximate methods for the score-constrained packing problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 847-859.
    4. Delorme, Maxence & Iori, Manuel & Martello, Silvano, 2016. "Bin packing and cutting stock problems: Mathematical models and exact algorithms," European Journal of Operational Research, Elsevier, vol. 255(1), pages 1-20.

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