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Mathematical models and decomposition algorithms for makespan minimization in plastic rolls production

Author

Listed:
  • Vitor Nesello
  • Maxence Delorme
  • Manuel Iori
  • Anand Subramanian

Abstract

We study an optimization problem that originates from the packaging industry, and in particular in the process of blown film extrusion, where a plastic film is used to produce rolls of different dimensions and colors. The film can be cut along its width, thus producing multiple rolls in parallel, and setup times must be considered when changing from one color to another. The optimization problem that we face is to produce a given set of rolls on a number of identical parallel machines by minimizing the makespan. The problem combines together cutting and scheduling decisions and is of high complexity. For its solution, we propose mathematical models and heuristic algorithms that involve a nontrivial decomposition method. By means of extensive computational experiments, we show that proven optimality can be achieved only on small instances, whereas for larger instances good quality solutions can be obtained especially by the use of an iterated local search algorithm.

Suggested Citation

  • Vitor Nesello & Maxence Delorme & Manuel Iori & Anand Subramanian, 2018. "Mathematical models and decomposition algorithms for makespan minimization in plastic rolls production," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(3), pages 326-339, March.
  • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:3:p:326-339
    DOI: 10.1057/s41274-017-0221-8
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    Citations

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    Cited by:

    1. Jianyu Long & Zhong Zheng & Xiaoqiang Gao & Panos M. Pardalos & Wanzhe Hu, 2020. "An effective heuristic based on column generation for the two-dimensional three-stage steel plate cutting problem," Annals of Operations Research, Springer, vol. 289(2), pages 291-311, June.
    2. Jean-François Côté & Mohamed Haouari & Manuel Iori, 2021. "Combinatorial Benders Decomposition for the Two-Dimensional Bin Packing Problem," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 963-978, July.
    3. Iori, Manuel & de Lima, Vinícius L. & Martello, Silvano & Miyazawa, Flávio K. & Monaci, Michele, 2021. "Exact solution techniques for two-dimensional cutting and packing," European Journal of Operational Research, Elsevier, vol. 289(2), pages 399-415.
    4. de Lima, Vinícius L. & Alves, Cláudio & Clautiaux, François & Iori, Manuel & Valério de Carvalho, José M., 2022. "Arc flow formulations based on dynamic programming: Theoretical foundations and applications," European Journal of Operational Research, Elsevier, vol. 296(1), pages 3-21.
    5. Mathijs Barkel & Maxence Delorme, 2023. "Arcflow Formulations and Constraint Generation Frameworks for the Two Bar Charts Packing Problem," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 475-494, March.
    6. Delorme, Maxence & Iori, Manuel & Mendes, Nilson F.M., 2021. "Solution methods for scheduling problems with sequence-dependent deterioration and maintenance events," European Journal of Operational Research, Elsevier, vol. 295(3), pages 823-837.
    7. Kramer, Arthur & Iori, Manuel & Lacomme, Philippe, 2021. "Mathematical formulations for scheduling jobs on identical parallel machines with family setup times and total weighted completion time minimization," European Journal of Operational Research, Elsevier, vol. 289(3), pages 825-840.

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