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A biased random key genetic algorithm for 2D and 3D bin packing problems

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  • Gonçalves, José Fernando
  • Resende, Mauricio G.C.

Abstract

In this paper we present a novel biased random-key genetic algorithm (BRKGA) for 2D and 3D bin packing problems. The approach uses a maximal-space representation to manage the free spaces in the bins. The proposed algorithm hybridizes a novel placement procedure with a genetic algorithm based on random keys. The BRKGA is used to evolve the order in which the boxes are packed into the bins and the parameters used by the placement procedure. Two new placement heuristics are used to determine the bin and the free maximal space where each box is placed. A novel fitness function that improves significantly the solution quality is also developed. The new approach is extensively tested on 858 problem instances and compared with other approaches published in the literature. The computational experiment results demonstrate that the new approach consistently equals or outperforms the other approaches and the statistical analysis confirms that the approach is significantly better than all the other approaches.

Suggested Citation

  • Gonçalves, José Fernando & Resende, Mauricio G.C., 2013. "A biased random key genetic algorithm for 2D and 3D bin packing problems," International Journal of Production Economics, Elsevier, vol. 145(2), pages 500-510.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:2:p:500-510
    DOI: 10.1016/j.ijpe.2013.04.019
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    5. Kurpel, Deidson Vitorio & Scarpin, Cassius Tadeu & Pécora Junior, José Eduardo & Schenekemberg, Cleder Marcos & Coelho, Leandro C., 2020. "The exact solutions of several types of container loading problems," European Journal of Operational Research, Elsevier, vol. 284(1), pages 87-107.
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    8. Bayliss, Christopher & Currie, Christine S.M. & Bennell, Julia A. & Martinez-Sykora, Antonio, 2019. "Dynamic pricing for vehicle ferries: Using packing and simulation to optimize revenues," European Journal of Operational Research, Elsevier, vol. 273(1), pages 288-304.
    9. R. M. A. Silva & M. G. C. Resende & P. M. Pardalos, 2015. "A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 710-728, October.
    10. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.
    11. Galrão Ramos, A. & Oliveira, José F. & Gonçalves, José F. & Lopes, Manuel P., 2016. "A container loading algorithm with static mechanical equilibrium stability constraints," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 565-581.
    12. Nestor M Cid-Garcia & Yasmin A Rios-Solis, 2020. "Positions and covering: A two-stage methodology to obtain optimal solutions for the 2d-bin packing problem," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-22, April.

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