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Combinatorial optimization methods for yarn dyeing planning

Author

Listed:
  • Ege Duran

    (University College Cork)

  • Cemalettin Ozturk

    (Munster Technological University)

  • M. Arslan Ornek

    (Yasar University)

Abstract

Managing yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

Suggested Citation

  • Ege Duran & Cemalettin Ozturk & M. Arslan Ornek, 2025. "Combinatorial optimization methods for yarn dyeing planning," Flexible Services and Manufacturing Journal, Springer, vol. 37(1), pages 282-319, March.
    • Handle: RePEc:spr:flsman:v:37:y:2025:i:1:d:10.1007_s10696-024-09541-1
    DOI: 10.1007/s10696-024-09541-1
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    References listed on IDEAS

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    1. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    2. Fleszar, Krzysztof & Hindi, Khalil S., 2018. "Algorithms for the unrelated parallel machine scheduling problem with a resource constraint," European Journal of Operational Research, Elsevier, vol. 271(3), pages 839-848.
    3. Debiao Li & Jing Wang & Rui Qiang & Raymond Chiong, 2021. "A hybrid differential evolution algorithm for parallel machine scheduling of lace dyeing considering colour families, sequence-dependent setup and machine eligibility," International Journal of Production Research, Taylor & Francis Journals, vol. 59(9), pages 2722-2738, May.
    4. Delorme, Maxence & Iori, Manuel & Martello, Silvano, 2016. "Bin packing and cutting stock problems: Mathematical models and exact algorithms," European Journal of Operational Research, Elsevier, vol. 255(1), pages 1-20.
    5. Li, Shuguang, 2017. "Parallel batch scheduling with inclusive processing set restrictions and non-identical capacities to minimize makespan," European Journal of Operational Research, Elsevier, vol. 260(1), pages 12-20.
    Full references (including those not matched with items on IDEAS)

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