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Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line

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  • Giard, Vincent
  • Jeunet, Jully

Abstract

This paper presents an integer programming formulation for the sequencing problem in mixed-model assembly lines where the number of temporarily hired utility workers and the number of sequence-dependent setups are to be optimized simultaneously through a cost function. The resultant model offers an operational way to implement the utility work needed to avoid line stoppages, unlike previous papers addressing the goal of smoothing the workload. The present research has an immediate application to the automotive industry, namely to the car-sequencing problem. Simulation results show that the proposed formulation leads to the optimum in a reasonable time for instances up to 15 items and to satisfactory feasible solutions for some of the larger problems we considered within a moderate time limit.

Suggested Citation

  • Giard, Vincent & Jeunet, Jully, 2010. "Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line," International Journal of Production Economics, Elsevier, vol. 123(2), pages 290-300, February.
    • Handle: RePEc:eee:proeco:v:123:y:2010:i:2:p:290-300
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    References listed on IDEAS

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    8. Korkmazel, Tugrul & Meral, Sedef, 2001. "Bicriteria sequencing methods for the mixed-model assembly line in just-in-time production systems," European Journal of Operational Research, Elsevier, vol. 131(1), pages 188-207, May.
    9. Andreas Drexl & Alf Kimms, 2001. "Sequencing JIT Mixed-Model Assembly Lines Under Station-Load and Part-Usage Constraints," Management Science, INFORMS, vol. 47(3), pages 480-491, March.
    10. Mansouri, S. Afshin, 2005. "A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines," European Journal of Operational Research, Elsevier, vol. 167(3), pages 696-716, December.
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    Cited by:

    1. Maximilian Stauder & Niklas Kühl, 2022. "AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 709-747, September.
    2. Florian Jaehn & Sergey Kovalev & Mikhail Y. Kovalyov & Erwin Pesch, 2014. "Multiproduct batching and scheduling with buffered rework: The case of a car paint shop," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(6), pages 458-471, September.
    3. Dolgui, Alexandre & Kovalev, Sergey & Kovalyov, Mikhail Y. & Malyutin, Sergey & Soukhal, Ameur, 2018. "Optimal workforce assignment to operations of a paced assembly line," European Journal of Operational Research, Elsevier, vol. 264(1), pages 200-211.
    4. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2023. "Markov decision process for multi-manned mixed-model assembly lines with walking workers," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Ramírez Palencia, Alberto E. & Mejía Delgadillo, Gonzalo E., 2012. "A computer application for a bus body assembly line using Genetic Algorithms," International Journal of Production Economics, Elsevier, vol. 140(1), pages 431-438.
    6. Rudolf, Gábor & Noyan, Nilay & Giard, Vincent, 2014. "Modeling sequence scrambling and related phenomena in mixed-model production lines," European Journal of Operational Research, Elsevier, vol. 237(1), pages 177-195.
    7. Yilmaz, Omer Faruk & Ozcelik, Gokhan & Yeni, Fatma Betul, 2020. "Lean holistic fuzzy methodology employing cross-functional worker teams for new product development projects: A real case study from high-tech industry," European Journal of Operational Research, Elsevier, vol. 282(3), pages 989-1010.
    8. Yildiz Kose & Hatice Nida Civan & Ertugrul Ayyildiz & Emre Cevikcan, 2022. "An Interval Valued Pythagorean Fuzzy AHP–TOPSIS Integrated Model for Ergonomic Assessment of Setup Process under SMED," Sustainability, MDPI, vol. 14(21), pages 1-30, October.

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