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Low-Cost Emergent Dynamic Scheduling for Flexible Job Shops

Author

Listed:
  • Yue Yin

    (School of Economics, Liaoning University, Shengyang 110036, China)

  • Xiao Kong

    (School of Economics, Liaoning University, Shengyang 110036, China)

  • Changqing Xia

    (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

  • Chi Xu

    (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

  • Xi Jin

    (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
    Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
    Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China)

Abstract

Flexible production is a typical representative of high-end manufacturing and is also a manifestation of a country’s national production capability. Compared with other production modes, the key distinction of flexible production is that all four dimensions of production (i.e., machines, operations, products, and orders) can be scheduled dynamically. Although many studies have investigated the flexible job shop scheduling problem, most have limited dynamic support and cannot deal with multidimensional dynamic production. This study, therefore, proposed a fine-grained system state description model, which was used to analyze the maximum production completion time. In the presence of a dynamic event, the model was able to quickly assign priorities to products according to the cost loss of each product. The system can therefore dynamically respond to events in a timely manner while reducing production costs and losses. Finally, we used a large number of orders to evaluate the proposed algorithm, which demonstrated millisecond-level response capability and low-cost maintenance capability. Compared with existing algorithms, the proposed algorithm reduced cost loss by up to 11 % .

Suggested Citation

  • Yue Yin & Xiao Kong & Changqing Xia & Chi Xu & Xi Jin, 2022. "Low-Cost Emergent Dynamic Scheduling for Flexible Job Shops," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1873-:d:827835
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    References listed on IDEAS

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    1. James R. Jackson, 1957. "Simulation research on job shop production," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 4(4), pages 287-295, December.
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    Cited by:

    1. Anran Zhao & Peng Liu & Xiyu Gao & Guotai Huang & Xiuguang Yang & Yuan Ma & Zheyu Xie & Yunfeng Li, 2022. "Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem," Mathematics, MDPI, vol. 10(23), pages 1-30, December.

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