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

Chunyang Yu, Wei Zhang, Xun Xu (), Yangjian Ji and Shiqiang Yu
Additional contact information
Chunyang Yu: Zhejiang University
Wei Zhang: University of Auckland
Xun Xu: University of Auckland
Yangjian Ji: Zhejiang University
Shiqiang Yu: University of Auckland

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 6, No 13, 1361 pages

Abstract: 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.

Keywords: Cloud manufacturing; Production planning; Service planning; Service encapsulation; Data mining; Time series (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-015-1184-8

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