• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2025, Vol. 61 ›› Issue (3): 23-39.doi: 10.3901/JME.2025.03.023

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Digital Shop Floor Manufacturing Capability Modeling and Adaptive Scheduling in Human-cyber-physical Interconnected Environment

HU Bingtao1, ZHONG Ruirui1, FENG Yixiong1,2, YANG Chen1,3, WANG Tianyue1,4, HONG Zhaoxi1, TAN Jianrong1   

  1. 1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027;
    2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025;
    3. China Unicom (Zhejiang) Industrial Internet Co., Ltd., Hangzhou 311199;
    4. Department of Systems Engineering, City University of Hong Kong, Hong Kong 518057
  • Received:2024-02-04 Revised:2024-08-25 Published:2025-03-12

Abstract: The development of Industry 5.0 presents higher requirements for the informatization, digitization, and intelligence of the manufacturing industry. To address the important challenges of the lack of traditional workshop manufacturing capacity organizational paradigm and intelligent scheduling technology, a digital workshop manufacturing capacity modeling and adaptive scheduling technology in the human-cyber-physical interconnected environment is proposed to achieve high-fidelity modeling of complex workshop manufacturing capacity and efficient scheduling of production resources. In order to effectively manage the production elements in the digital workshop, a digital workshop manufacturing capacity modeling technology that integrates the human-cyber-physical system is proposed. In addition, a deep reinforcement learning-driven adaptive scheduling algorithm (DRL-AS) is devised for the digital workshop, which models the flexible job shop scheduling problem in the form of heterogeneous directed acyclic graphs. Considering the complex coupling relationship between operations and machines, a multi-factor representation method based on hierarchical self-attention mechanism is designed to extract global features of the environmental state and assist the agent in making high-quality decisions. Proximal policy optimization (PPO) algorithm is used to train the proposed adaptive scheduling technology. Experimental results show that the scheduling performance and generalization performance of the proposed method are significantly better than those of the comparison algorithms.

Key words: human-cyber-physical system, flexible job-shop scheduling, deep reinforcement learning, manufacturing capability modeling, digital twin

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