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Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information

Author

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  • Jiewu Leng

    (Xi’an Jiaotong University)

  • Pingyu Jiang

    (Xi’an Jiaotong University)

Abstract

Since discrete manufacturing system (DMS) is a complicated dynamic network that comprises of processes, machines, and work in process, a coherent methodology for performance tracking and sustainable improvement at the system/network level is of great significance for manufacturers to respond rapidly in a mass customization paradigm. Fortunately, the radio frequency identification (RFID) technologies provide us the real-time tracking ability of the production process that suffers unpredictable and recessive disturbances. This paper proposes a dynamic scheduling approach based on multi-layer network metrics of RFID-driven DMS. Firstly, considering the elements of DMS (e.g., parts, manufacturing activities, and equipment) and relationships among them, a DMS model named complex manufacturing network (CMN) is proposed. Then, several multi-layer network metrics of the CMN are defined and analysed. The implications of these metrics lead to a better understanding of the current status and performance of DMS. Thirdly, a dynamic scheduling algorithm using these metrics as heuristic information is proposed to solve multi-resources and independent-task DMS. Finally, a Printing Machine manufacturing system is chosen as an example to illustrate the feasibility of the proposed approach.

Suggested Citation

  • Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1301-y
    DOI: 10.1007/s10845-017-1301-y
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    References listed on IDEAS

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    Cited by:

    1. Diego Augusto Jesus Pacheco & Carlos Fernando Jung & Marcelo Cunha Azambuja, 2023. "Towards industry 4.0 in practice: a novel RFID-based intelligent system for monitoring and optimisation of production systems," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1165-1181, March.
    2. Zaifang Zhang & Darao Xu & Egon Ostrosi & Hui Cheng, 2020. "Optimization of the Product–Service System Configuration Based on a Multilayer Network," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    3. Leng, Jiewu & Ruan, Guolei & Jiang, Pingyu & Xu, Kailin & Liu, Qiang & Zhou, Xueliang & Liu, Chao, 2020. "Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Wang Shijie & Zhang Yingfeng, 2021. "A credit-based dynamical evaluation method for the smart configuration of manufacturing services under Industrial Internet of Things," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1091-1115, April.
    5. Shaohua Huang & Yu Guo & Nengjun Yang & Shanshan Zha & Daoyuan Liu & Weiguang Fang, 2021. "A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1845-1861, October.

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