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Data mining based multi-level aggregate service planning for cloud manufacturing

Author

Listed:
  • Chunyang Yu

    (Zhejiang University
    University of Auckland)

  • Wei Zhang

    (University of Auckland)

  • Xun Xu

    (University of Auckland)

  • Yangjian Ji

    (Zhejiang University)

  • Shiqiang Yu

    (University of Auckland)

Abstract

Cloud manufacturing (CMfg) promotes a dynamic distributed manufacturing environment by connecting the service providers and manages them in a centralized way. Due to the distinct production capabilities, the service providers tend to be delegated services of different granularities. Meanwhile, users of different types may be after services of different granularities. A traditional aggregate production planning method is often incapable of dealing with type of problems. For this reason, a multi-level aggregate service planning (MASP) methodology is proposed. The MASP service hierarchy is presented, which integrates the services of different granularities into a layered structure. Based on this structure, one of data mining technologies named time series is introduced to provide dynamic forecast for each layer. In this way, MASP can not only deal with the services of multi-granularity, but also meet the requirements of all related service providers irrespective of their manufacturing capabilities. A case study has been carried out, showing how MASP can be applied in a CMfg environment. The results of the prediction are considered reliable as the order of magnitude of the production for each service layer is much greater than that of the corresponding mean forecast error.

Suggested Citation

  • Chunyang Yu & Wei Zhang & Xun Xu & Yangjian Ji & Shiqiang Yu, 2018. "Data mining based multi-level aggregate service planning for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1351-1361, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1184-8
    DOI: 10.1007/s10845-015-1184-8
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    References listed on IDEAS

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    Cited by:

    1. Anupama Prashar, 2023. "Title: production planning and control in industry 4.0 environment: a morphological analysis of literature and research agenda," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2513-2528, August.
    2. Wang, Jing, 2021. "Research on sustainable evolution of China's cloud manufacturing policies," Technology in Society, Elsevier, vol. 66(C).
    3. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.

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