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A genetic algorithm approach for the cutting stock problem

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Abstract

In this paper, a genetic algorithm approach is developed for solving the rectangular cutting stock problem. The performance measure is the minimization of the waste. Simulation results obtained from the genetic algorithm-based approach are compared with one heuristic based on partial enumeration of all feasible patterns, and another heuristic based on a genetic neuro-nesting approach. Some test problems taken from the literature were used for the experimentation. Finally, the genetic algorithm approach was applied to test problems generated randomly. The simulation results of the proposed approach in terms of solution quality are encouraging when compared to the partial enumeration-based heuristic and the genetic neuro-nesting approach.

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References

  • Babu, A. R. and Babu, N. R. (1999) Effective nesting of rectangular parts in multiple rectangular sheets using genetic and heuristic algorithms. International Journal of Production Research, 37(7), 1625-1643.

    Google Scholar 

  • Benati, S. (1997) An algorithm for a cutting stock problem on a strip. Journal of Operational Research Society, 48, 288-294.

    Google Scholar 

  • Berkey, J. O. and Wang, P. Y. (1987) Two-dimensional final bin-packing algorithms. Journal of Operational Research Society, 38, 423-429.

    Google Scholar 

  • Chauny, F., Loulou, R., Sadones, S. and Soumis, F. (1991) A two-phase heuristic for the two-dimensional cutting-stock problem. Journal of Operational Research Society, 42, 32-47.

    Google Scholar 

  • Dagli, C. H. and Poshyanonda, P. (1997) New approaches to nesting rectangular patterns. Journal of Intelligent Manufacturing, 8, 177-190.

    Google Scholar 

  • Dorigo, M. and Gambardella, L. M. (1997) Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Transaction on Evolutionary Computation 1(10), 53-66.

    Google Scholar 

  • Fleurent, C. and Ferland, J. (1994) Genetic hybrids for the quadratic assignment problem. DIMACS. Series in Mathematical Theoretical Computer Science, 16, 190-206.

    Google Scholar 

  • Garey, M. R. and Johnson, D. S. (1979) Computers and intractability: a guide to the Theory of NP-Completeness, W. H. Freeman and Company, San Francisco, CA, USA.

    Google Scholar 

  • Gilmore, P. C. and Gomory, R. E. (1963a) A linear programming approach to the cutting stock problem, Part II. Operations Research, 11, 863-888.

    Google Scholar 

  • Gilmore, P. C. and Gomory, R. E. (1963b) The theory and the computation of knapsack functions. Operational Research, 14, 1045-1074.

    Google Scholar 

  • Gilmore, P. C. and Gomory, R. E. (1966) Multi-stage cutting stock problems of two and more dimensions. Operational Research, 13, 94-120.

    Google Scholar 

  • Glover, F. (1989) Tabu search-Part I, ORSA Journal of Computing, 1, 190-206.

    Google Scholar 

  • Goldberg, D. E. (1989) Genetic Algorithms: In Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Hains, M. J. and Freeman, H. (1970) A multistage solution of the template-layout problem. IEEE Transactions on System, Science and Cybernetics, SSC-6, 145-151.

    Google Scholar 

  • Kroger, B. (1995) Guillotinable bin packing: a genetic approach. European Journal of Operational Research, 84, 645-661.

    Google Scholar 

  • Liepins, G. E. and Hilliard, M. R. (1986) Genetic algorithms as a paradigm for machine learning. Paper Presented at the ORSA/IMS Joint National Meeting, Miami, FL, USA.

  • Onwubolu, G. C. and Mutingi, M. (2001) Optimizing the multiple constrained resources product mix problem using genetic algorithms. International Journal of Production Research, 39(9), 1897-1910.

    Google Scholar 

  • Paradza Daza, V., Munoz, R. de-A. and Gomes, A. (1995) A hybrid genetic algorithm for the two-dimensional guillotine cutting problem, in Biethan, J. and Nissen, V. (eds.) Evolutionary Algorithms in Management Applications, Berlin, pp. 183-196.

  • Poshyanonda, P., Bahrami, A. and Dagli, C. (1992) Artificial neural networks in stock cutting problem, in Neural Networks in Manufacturing and Robotics, Shin, Y. C., Abodelmonen, A. H. and Kumara, S. (eds.) ASME Press, New York, pp. 143-153.

    Google Scholar 

  • Spears, W. M. and De Jong, K. A. (1990) Using neural networks and genetic algorithms as heuristics for NP-complete problems, in Proceedings of the International Joint Conference on Neural Networks IEEE, 1, 118-123.

    Google Scholar 

  • Wang, P. Y. (1983) Two algorithms for constrained two-dimensional cutting stock problems. Operations Research, 31, 573-586.

    Google Scholar 

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Authors and Affiliations

  1. Department of Engineering, The University of the South Pacific, P.O. Box 1168, Suva, Fiji

    Godfrey C. Onwubolu

  2. Olivine Industries, Zimbabwe

    Michael Mutingi

Authors
  1. Godfrey C. Onwubolu
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  2. Michael Mutingi
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Onwubolu, G.C., Mutingi, M. A genetic algorithm approach for the cutting stock problem. Journal of Intelligent Manufacturing 14, 209–218 (2003). https://doi.org/10.1023/A:1022955531018

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  • DOI: https://doi.org/10.1023/A:1022955531018

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