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Computational modelling of manufacturing choice complexity in a mixed-model assembly line

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  • Moise Busogi
  • Kasin Ransikarbum
  • Yeong Gwang Oh
  • Namhun Kim

Abstract

Manufacturing systems have evolved to adopt a mixed-model assembly line enabling the production of high product variety. Although the mixed-model assembly system with semi-automation (i.e. human involvement) can offer a wide range of advantages, the system becomes very complex as variety increases. Further, while the complexity from different options can worsen the system performance, there is a lack of quantifiable models for manufacturing complexity in the literature. Thus, in this paper, we propose a novel method to quantify manufacturing choice complexity for the effective management of semi-automated systems in a mixed-model assembly line. Based on the concept of information entropy, our model considers both the options mix and the similarities between options. The proposed model, along with an illustrative case study, not only serves as a tool to quantitatively assess the impact of choice complexity on total system performance, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.

Suggested Citation

  • Moise Busogi & Kasin Ransikarbum & Yeong Gwang Oh & Namhun Kim, 2017. "Computational modelling of manufacturing choice complexity in a mixed-model assembly line," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5976-5990, October.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:20:p:5976-5990
    DOI: 10.1080/00207543.2017.1319088
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    Cited by:

    1. Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.

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