Data-driven cloud simulation architecture for automated flexible production lines: application in real smart factories

Dan Luo, Zailin Guan, Cong He, Yeming Gong () and Lei Yue
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Dan Luo: HUST - Huazhong University of Science and Technology [Wuhan]
Zailin Guan: HUST - Huazhong University of Science and Technology [Wuhan]
Cong He: HUST - Huazhong University of Science and Technology [Wuhan]
Yeming Gong: EM - EMLyon Business School
Lei Yue: Guangzhou University

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Abstract: In recent years, more manufacturing enterprises are building automated flexible production lines (AFPLs) to satisfy the dynamic and diversified demand. Currently, static planning methods can hardly meet the requirements of the dynamic resource allocation for AFPLs. The technologies of the digital twin can help solve dynamic problems. Therefore, we propose a data-driven cloud simulation architecture for AFPLs in smart factories. First, we design a cloud simulation platform as the architecture foundation. Second, we use the data-driven modelling and simulation method to achieve automated modelling. Third, we implement the system on the cloud using Java, MySQL, and the Anylogic platform, and verify the efficiency of the proposed method by experiments in the real workshop of a 3C (Computer, Communication, Consumer electronics) company. The experimental results show the proposed architecture can support the real-time resource allocation decisions to maximise the throughput in AFPLs. This paper makes contributions by proposing an architecture realising automatic modelling and data-driven simulation first in the cloud simulation environment, and filling the gap of dynamic resource allocation in the research of AFPLs.

Keywords: AI; Smart Manufacturing (search for similar items in EconPapers)
Date: 2022-06-01
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Citations: View citations in EconPapers (1)

Published in International Journal of Production Research, 2022, 60 (12), 3751-3773 p. ⟨10.1080/00207543.2021.1931977⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04325622

DOI: 10.1080/00207543.2021.1931977

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