Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

被引:0
作者
Ahn, Jangsu [1 ]
Yun, Seongjin [1 ]
Kwon, Jin-Woo [1 ]
Kim, Won-Tae [1 ]
机构
[1] Korea Univ Technol & Educ, Dept Comp Sci & Engn, Future Convergence Engn Major, Cheonan 31253, South Korea
关键词
industrial metaverse; federated digital twins; large language model; flexible job-shop; scheduling; the software-defined factory; MASS CUSTOMIZATION;
D O I
10.3390/electronics13224452
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements.
引用
收藏
页数:15
相关论文
共 30 条
[11]   Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization [J].
Hu, S. Jack .
FORTY SIXTH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2013, 2013, 7 :3-8
[12]   The assembly line at Ford and transportation platforms: A historical comparison of labour process reorganisation [J].
Hughes, Carl .
NEW TECHNOLOGY WORK AND EMPLOYMENT, 2025, 40 (01) :41-59
[13]   Optimal scheduling for sequentially connected cluster tools with dual-armed robots and a single input and output module [J].
Kim, Dae-Kyu ;
Kim, Hyun-Jung ;
Lee, Tae-Eog .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2017, 55 (11) :3092-3109
[14]  
Koren Y., 2010, The Global Manufacturing Revolution, P227, DOI DOI 10.1002/9780470618813.CH9
[15]   A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem [J].
Lei, Kun ;
Guo, Peng ;
Zhao, Wenchao ;
Wang, Yi ;
Qian, Linmao ;
Meng, Xiangyin ;
Tang, Liansheng .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 205
[16]  
Ling Wang, 2021, Complex System Modeling and Simulation, V1, P257, DOI 10.23919/CSMS.2021.0027
[17]   Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems [J].
Liu, Chien-Liang ;
Chang, Chuan-Chin ;
Tseng, Chun-Jan .
IEEE ACCESS, 2020, 8 :71752-71762
[18]   Joint Knowledge Graph and Large Language Model for Fault Diagnosis and Its Application in Aviation Assembly [J].
Liu, Peifeng ;
Qian, Lu ;
Zhao, Xingwei ;
Tao, Bo .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 20 (06) :8160-8169
[19]  
Ziegler DM, 2020, Arxiv, DOI arXiv:1909.08593
[20]   Digital twin in manufacturing: conceptual framework and case studies [J].
Onaji, Igiri ;
Tiwari, Divya ;
Soulatiantork, Payam ;
Song, Boyang ;
Tiwari, Ashutosh .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2022, 35 (08) :831-858