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Resequencing mixed-model assembly lines with restoration to customer orders

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  • Taube, F.
  • Minner, S.

Abstract

We consider a supplier who delivers modules to an original equipment manufacturer (OEM) just-in-time and just-in-sequence. Production at the supplier is done via a mixed-model assembly line. The time between knowing the OEM sequence and delivering the finished workpieces to the OEM is small. Nonetheless, resequencing for the mixed-model assembly line at the supplier might be advantageous under various objectives such as workload balancing, leveling of materials consumption or color batching. However, if resequencing is done, the effort to restore the original OEM sequence should be small to achieve this in time. We propose a model for optimizing resequencing under the condition that restoring the original sequence is achieved via a first-in-first-out (FIFO) strategy, where workpieces are stored in mix banks at the end of production and only the workpieces at the front of those banks have to be dispatched in order to rebuild the original sequence. The model is the combination of an assignment or traveling salesman and a vehicle routing problem. We adapt the load balancing, material leveling, and color batching problem from the sequencing literature to our formulation and present numerical results derived from a controlled testbed. They show that, compared to producing the OEM sequence as-is, huge savings in the objective values ( > 50% on average), are made. Furthermore, a limited lookahead approach leads to good solutions in just a small number of seconds, even for large scale problems.

Suggested Citation

  • Taube, F. & Minner, S., 2018. "Resequencing mixed-model assembly lines with restoration to customer orders," Omega, Elsevier, vol. 78(C), pages 99-111.
  • Handle: RePEc:eee:jomega:v:78:y:2018:i:c:p:99-111
    DOI: 10.1016/j.omega.2017.11.006
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    References listed on IDEAS

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    1. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
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    8. A Lim & Z Xu, 2009. "Searching optimal resequencing and feature assignment on an automated assembly line," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 361-371, March.
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    10. Chica, Manuel & Bautista, Joaquín & Cordón, Óscar & Damas, Sergio, 2016. "A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand," Omega, Elsevier, vol. 58(C), pages 55-68.
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    Cited by:

    1. Tobias Kreiter & Ulrich Pferschy, 2020. "Integer programming models versus advanced planning business software for a multi-level mixed-model assembly line problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 1141-1177, September.
    2. Li, Yuchen & Liu, Ming & Saldanha-da-Gama, Francisco & Yang, Zaoli, 2024. "Risk-averse two-stage stochastic programming for assembly line reconfiguration with dynamic lot sizes," Omega, Elsevier, vol. 127(C).
    3. Hashemi-Petroodi, S. Ehsan & Thevenin, Simon & Kovalev, Sergey & Dolgui, Alexandre, 2022. "Model-dependent task assignment in multi-manned mixed-model assembly lines with walking workers," Omega, Elsevier, vol. 113(C).

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