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A Heuristic Programming Solution to a Nonlinear Cutting Stock Problem

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  • Robert W. Haessler

    (Wright State University)

Abstract

A heuristic procedure for scheduling production rolls of paper through a finishing operation to cut them down to finished roll sizes is described. The ratio of service time to interarrival time of production rolls at the initial cutting station is large so that insufficient time is available to set it up unless a minimum number of production rolls are to be processed in the same manner. Otherwise, some portion of each production roll must go through a reprocessing operation to complete the cutting of finished sizes. The objective is to minimize the cost of trim loss and reprocessing. The procedure generates cutting patterns and usage levels sequentially until all the requirements are satisfied. At each step the search is dependent upon the characteristics of the unsatisfied requirements. A maximum of three solutions is generated for each problem. If none satisfies a predetermined aspiration level, the best of the three is chosen. The procedure was evaluated by scheduling a specific paper production facility and observing the results for a set of 15 problems. For each problem, the best solution was recorded. The overall results from this set of problems were then compared to previously recorded results on problems solved manually. There was a 16% improvement in solution quality for the heuristic procedure relative to the manual method.

Suggested Citation

  • Robert W. Haessler, 1971. "A Heuristic Programming Solution to a Nonlinear Cutting Stock Problem," Management Science, INFORMS, vol. 17(12), pages 793-802, August.
  • Handle: RePEc:inm:ormnsc:v:17:y:1971:i:12:p:b793-b802
    DOI: 10.1287/mnsc.17.12.B793
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    Cited by:

    1. Maimon, Oded & Dayagi, Arie, 1995. "Nesting planning based on production priorities and technological efficiency," European Journal of Operational Research, Elsevier, vol. 80(1), pages 121-129, January.
    2. 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.
    3. Westerlund, Tapio & Isaksson, Johnny & Harjunkoski, Iiro, 1998. "Solving a two-dimensional trim-loss problem with MILP," European Journal of Operational Research, Elsevier, vol. 104(3), pages 572-581, February.
    4. C Alves & J M Valério de Carvalho, 2008. "New integer programming formulations and an exact algorithm for the ordered cutting stock problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1520-1531, November.
    5. Harjunkoski, Iiro & Westerlund, Tapio & Porn, Ray & Skrifvars, Hans, 1998. "Different transformations for solving non-convex trim-loss problems by MINLP," European Journal of Operational Research, Elsevier, vol. 105(3), pages 594-603, March.
    6. Nonas, Sigrid Lise & Thorstenson, Anders, 2000. "A combined cutting-stock and lot-sizing problem," European Journal of Operational Research, Elsevier, vol. 120(2), pages 327-342, January.
    7. Felix Prause & Kai Hoppmann-Baum & Boris Defourny & Thorsten Koch, 2021. "The maximum diversity assortment selection problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(3), pages 521-554, June.
    8. Muter, İbrahim & Sezer, Zeynep, 2018. "Algorithms for the one-dimensional two-stage cutting stock problem," European Journal of Operational Research, Elsevier, vol. 271(1), pages 20-32.
    9. Umetani, Shunji & Yagiura, Mutsunori & Ibaraki, Toshihide, 2003. "One-dimensional cutting stock problem to minimize the number of different patterns," European Journal of Operational Research, Elsevier, vol. 146(2), pages 388-402, April.
    10. Sesh Murthy & Rama Akkiraju & Richard Goodwin & Pinar Keskinocak & John Rachlin & Frederick Wu & James Yeh & Robert Fuhrer & Santhosh Kumaran & Alok Aggarwal & Martin Sturzenbecker & Ranga Jayaraman &, 1999. "Cooperative Multiobjective Decision Support for the Paper Industry," Interfaces, INFORMS, vol. 29(5), pages 5-30, October.
    11. Leão, Aline A.S. & Santos, Maristela O. & Hoto, Robinson & Arenales, Marcos N., 2011. "The constrained compartmentalized knapsack problem: mathematical models and solution methods," European Journal of Operational Research, Elsevier, vol. 212(3), pages 455-463, August.
    12. Beraldi, P. & Bruni, M.E. & Conforti, D., 2009. "The stochastic trim-loss problem," European Journal of Operational Research, Elsevier, vol. 197(1), pages 42-49, August.
    13. Zak, Eugene J., 2002. "Modeling multistage cutting stock problems," European Journal of Operational Research, Elsevier, vol. 141(2), pages 313-327, September.
    14. Kallrath, Julia & Rebennack, Steffen & Kallrath, Josef & Kusche, Rüdiger, 2014. "Solving real-world cutting stock-problems in the paper industry: Mathematical approaches, experience and challenges," European Journal of Operational Research, Elsevier, vol. 238(1), pages 374-389.
    15. Suliman, Saad M. A., 2001. "Pattern generating procedure for the cutting stock problem," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 293-301, December.
    16. Hajizadeh, Iman & Lee, Chi-Guhn, 2007. "Alternative configurations for cutting machines in a tube cutting mill," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1385-1396, December.
    17. Tao Wu & Kerem Akartunal? & Raf Jans & Zhe Liang, 2017. "Progressive Selection Method for the Coupled Lot-Sizing and Cutting-Stock Problem," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 523-543, August.
    18. Jie Fang & Yunqing Rao & Qiang Luo & Jiatai Xu, 2023. "Solving One-Dimensional Cutting Stock Problems with the Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
    19. Gramani, Maria Cristina N. & França, Paulo M. & Arenales, Marcos N., 2011. "An Exact Approach to the Relaxed Combined Production Planning Model," Insper Working Papers wpe_178, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

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