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Heterogeneous demand–capacity synchronization for smart assembly cell line based on artificial intelligence-enabled IIoT

Author

Listed:
  • Shiquan Ling

    (The University of Hong Kong
    Southern University of Science and Technology)

  • Daqiang Guo

    (The University of Hong Kong
    University of Cambridge)

  • Mingxing Li

    (Jinan University (Zhuhai Campus))

  • Yiming Rong

    (Southern University of Science and Technology)

  • George Q. Huang

    (The Hong Kong Polytechnic University)

Abstract

An assembly cell line (ACL) is one type of cell production practice, derived from the Toyota Production System in the electronics industry and rapidly spread to other fields. In this mode, the conveyor line is divided into assembly cells (ACs) where various parts and tools are placed closer to the workers, enabling them to perform multiple tasks throughout an entire product assembly from start to finish. In this way, ACL allows manufacturers to rapidly configure an appropriate heterogeneous capacity to match heterogeneous demands with diversified customer orders in the high-mix, low-volume (HMLV) environment, which is the spread of the Just-In-Time (JIT) philosophy from the material level to the organization level. However, due to the lack of real-time information sharing in the ACL workshop, especially the up-to-date individual capacity and asynchronous production processes within and between ACs, it is hard to coordinate the heterogeneous capacities of ACs to meet the HMLV demands in a complex manufacturing environment with uncertainties. In this context, this paper proposes a heterogeneous demand–capacity synchronization (HDCS) for smart ACL by using artificial intelligence-enabled IIoT (AIoT) technologies, in which computer vision (CV) is applied for up-to-date capacity analysis of ACs. Based on these, an AIoT-enabled Graduation Intelligent Manufacturing System (GiMS) with feedback loops is developed to support real-time information sharing for the synchronous coordination of the ACL operation, which also provides the basis for the implementation of the HDCS mechanism through a rolling scheduling approach. Finally, a real-life industrial case is carried out by a proof-of-concept prototype to verify the proposed approach, and the results show that the measures on shipment punctuality and production efficiency are both significantly improved.

Suggested Citation

  • Shiquan Ling & Daqiang Guo & Mingxing Li & Yiming Rong & George Q. Huang, 2024. "Heterogeneous demand–capacity synchronization for smart assembly cell line based on artificial intelligence-enabled IIoT," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 539-554, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-022-02050-8
    DOI: 10.1007/s10845-022-02050-8
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    References listed on IDEAS

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    1. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Scott M. Shafer & David A. Nembhard & Mustafa V. Uzumeri, 2001. "The Effects of Worker Learning, Forgetting, and Heterogeneity on Assembly Line Productivity," Management Science, INFORMS, vol. 47(12), pages 1639-1653, December.
    3. Glock, C. H. & Grosse, E. H. & Neumann, W. P. & Sgarbossa, F., 2017. "Editorial: Human factors in industrial and logistic system design," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 89304, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Daqiang Guo & Ray Y. Zhong & Shiquan Ling & Yiming Rong & George Q. Huang, 2020. "A roadmap for Assembly 4.0: self-configuration of fixed-position assembly islands under Graduation Intelligent Manufacturing System," International Journal of Production Research, Taylor & Francis Journals, vol. 58(15), pages 4631-4646, July.
    5. Dario Ikuo Miyake, 2006. "The shift from belt conveyor line to work-cell based assembly systems to cope with increasing demand variation in Japanese industries," International Journal of Automotive Technology and Management, Inderscience Enterprises Ltd, vol. 6(4), pages 419-439.
    6. Ron Adner & Daniel Levinthal, 2001. "Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation," Management Science, INFORMS, vol. 47(5), pages 611-628, May.
    7. Ahmad Barari & Marcos Sales Guerra Tsuzuki & Yuval Cohen & Marco Macchi, 2021. "Editorial: intelligent manufacturing systems towards industry 4.0 era," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1793-1796, October.
    8. Becker, Christian & Scholl, Armin, 2006. "A survey on problems and methods in generalized assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 694-715, February.
    9. Kathryn E. Stecke & Yong Yin & Ikou Kaku & Yasuhiko Murase, 2012. "Seru: The Organizational Extension of JIT for a Super-Talent Factory," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 3(1), pages 106-119, January.
    10. Jian Chen & Meilin Wang & Xiang T. R. Kong & George Q. Huang & Qinyun Dai & Guoqiang Shi, 2019. "Manufacturing synchronization in a hybrid flowshop with dynamic order arrivals," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2659-2668, October.
    11. Peng Lin & Leidi Shen & Zhiheng Zhao & George Q. Huang, 2019. "Graduation manufacturing system: synchronization with IoT-enabled smart tickets," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2885-2900, December.
    12. Cheng Qian & Yingfeng Zhang & Chen Jiang & Shenle Pan & Yiming Rong, 2020. "A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing," Post-Print hal-02190419, HAL.
    13. Yong Yin & Kathryn E. Stecke & Dongni Li, 2018. "The evolution of production systems from Industry 2.0 through Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 848-861, January.
    14. Stadtler, Hartmut, 2003. "Multilevel lot sizing with setup times and multiple constrained resources: Internally rolling schedules with lot-sizing windows," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 20204, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Stanislav Chankov & Marc-Thorsten Hütt & Julia Bendul, 2018. "Influencing factors of synchronization in manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4781-4801, July.
    16. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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