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Modeling sequence scrambling and related phenomena in mixed-model production lines

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  • Rudolf, Gábor
  • Noyan, Nilay
  • Giard, Vincent

Abstract

In this paper we examine the various effects that workstations and rework loops with identical parallel processors and stochastic processing times have on the performance of a mixed-model production line. Of particular interest are issues related to sequence scrambling. In many production systems (especially those operating on just-in-time or in-line vehicle sequencing principles), the sequence of orders is selected carefully to optimize line efficiency while taking into account various line balancing and product spacing constraints. However, this sequence is often altered due to stochastic factors during production. This leads to significant economic consequences, due to either the degraded performance of the production line, or the added cost of restoring the sequence (via the use of systems such as mix banks or automated storage and retrieval systems). We develop analytical formulas to quantify both the extent of sequence scrambling caused by a station of the production line, and the effects of this scrambling on downstream performance. We also develop a detailed Markov chain model to analyze related issues regarding line stoppages and throughput. We demonstrate the usefulness of our methods on a range of illustrative numerical examples, and discuss the implications from a managerial point of view.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:237:y:2014:i:1:p:177-195
    DOI: 10.1016/j.ejor.2014.02.041
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    References listed on IDEAS

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    1. 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.
    2. Alford, Dave & Sackett, Peter & Nelder, Geoff, 2000. "Mass customisation -- an automotive perspective," International Journal of Production Economics, Elsevier, vol. 65(1), pages 99-110, April.
    3. repec:dau:papers:123456789/2861 is not listed on IDEAS
    4. Xiaobo, Zhao & Xu, Deju & Zhang, Hanqin & He, Qi-Ming, 2007. "Modeling and analysis of a supply-assembly-store chain," European Journal of Operational Research, Elsevier, vol. 176(1), pages 275-294, January.
    5. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2009. "Sequencing mixed-model assembly lines: Survey, classification and model critique," European Journal of Operational Research, Elsevier, vol. 192(2), pages 349-373, January.
    6. Roodbergen, Kees Jan & Vis, Iris F.A., 2009. "A survey of literature on automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 194(2), pages 343-362, April.
    7. repec:dau:papers:123456789/352 is not listed on IDEAS
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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.

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