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Approximation Algorithms to Solve Real-Life Multicriteria Cutting Stock Problems

Author

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  • Chengbin Chu

    (Département GSI, Université de Technologie de Troyes, 12 rue Marie Curie-BP 2060, 10010 Troyes Cedex, France)

  • Julien Antonio

    (INRIA-Lorraine, CESCOM Technopôle Metz 2 000, 4 rue Marconi, 57070 Metz, France)

Abstract

This paper addresses a real-life unidimensional cutting stock problem. The objective is not only to minimize trim loss, as in traditional cutting stock problems, but also to minimize cutting time. A variety of technical constraints are taken into account. These constraints often arise in the iron and steel cutting industry. Since cutting stock problems are well known to be NP-hard, it is prohibitive to obtain optimal solutions. We develop approximation algorithms for different purposes: quick response algorithms for individual customer requirement planning to build a quotation, and elaborate algorithms to provide a production plan for the next day. These latter algorithms are submitted to less strict computation time limitations. Computational results show that our algorithms improve by 8% the performance of our partner company where the cutting plan had been carried out manually by very experienced people. Numerical comparison for small sized problems shows that these algorithms provide solutions very close to optimal. These algorithms have been implemented in the company.

Suggested Citation

  • Chengbin Chu & Julien Antonio, 1999. "Approximation Algorithms to Solve Real-Life Multicriteria Cutting Stock Problems," Operations Research, INFORMS, vol. 47(4), pages 495-508, August.
    • Handle: RePEc:inm:oropre:v:47:y:1999:i:4:p:495-508
    DOI: 10.1287/opre.47.4.495
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    References listed on IDEAS

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    Cited by:

    1. Song, X. & Chu, C.B. & Nie, Y.Y. & Bennell, J.A., 2006. "An iterative sequential heuristic procedure to a real-life 1.5-dimensional cutting stock problem," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1870-1889, December.
    2. P A Huegler & J C Hartman, 2007. "Fulfilling orders for steel plates from existing inventory," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(9), pages 1156-1166, September.
    3. Lixin Tang & Ying Meng & Zhi-Long Chen & Jiyin Liu, 2016. "Coil Batching to Improve Productivity and Energy Utilization in Steel Production," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 262-279, May.
    4. Cherri, Adriana Cristina & Arenales, Marcos Nereu & Yanasse, Horacio Hideki & Poldi, Kelly Cristina & Gonçalves Vianna, Andréa Carla, 2014. "The one-dimensional cutting stock problem with usable leftovers – A survey," European Journal of Operational Research, Elsevier, vol. 236(2), pages 395-402.
    5. Fatma Serab ONURSAL & Alpaslan FIÐLALI & Yekta KAYMAN, 2015. "A Model For Optimizing Material Assortment," Eurasian Business & Economics Journal, Eurasian Academy Of Sciences, vol. 2(2), pages 76-92, July.
    6. Arbib, Claudio & Marinelli, Fabrizio, 2005. "Integrating process optimization and inventory planning in cutting-stock with skiving option: An optimization model and its application," European Journal of Operational Research, Elsevier, vol. 163(3), pages 617-630, June.
    7. Wang, Danni & Xiao, Fan & Zhou, Lei & Liang, Zhe, 2020. "Two-dimensional skiving and cutting stock problem with setup cost based on column-and-row generation," European Journal of Operational Research, Elsevier, vol. 286(2), pages 547-563.
    8. Claudio Arbib & Fabrizio Marinelli & Fabrizio Rossi & Francesco Di Iorio, 2002. "Cutting and Reuse: An Application from Automobile Component Manufacturing," Operations Research, INFORMS, vol. 50(6), pages 923-934, December.
    9. Alyne Toscano & Socorro Rangel & Horacio Hideki Yanasse, 2017. "A heuristic approach to minimize the number of saw cycles in small-scale furniture factories," Annals of Operations Research, Springer, vol. 258(2), pages 719-746, November.
    10. Stefano Gualandi & Federico Malucelli, 2013. "Constraint Programming-based Column Generation," Annals of Operations Research, Springer, vol. 204(1), pages 11-32, April.

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