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 50 条
  • [1] Deep Reinforcement Learning-Based QoS Optimization for Software-Defined Factory Heterogeneous Networks
    Xia, Dan
    Wan, Jiafu
    Xu, Pengpeng
    Tan, Jinbiao
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2022, 19 (04): : 4058 - 4068
  • [2] Deep Reinforcement Learning-Based Routing on Software-Defined Networks
    Kim, Gyungmin
    Kim, Yohan
    Lim, Hyuk
    IEEE ACCESS, 2022, 10 : 18121 - 18133
  • [3] A Deep Reinforcement Learning-based Trust Management Scheme for Software-defined Vehicular Networks
    Zhang, Dajun
    Yu, F. Richard
    Yang, Ruizhe
    Tang, Helen
    DIVANET'18: PROCEEDINGS OF THE 8TH ACM SYMPOSIUM ON DESIGN AND ANALYSIS OF INTELLIGENT VEHICULAR NETWORKS AND APPLICATIONS, 2018, : 1 - 7
  • [4] Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT
    Zhu, Xiaojuan
    Zhang, Tianhao
    Zhang, Jinwei
    Zhao, Bao
    Zhang, Shunxiang
    Wu, Cai
    COMPUTER NETWORKS, 2023, 235
  • [5] A Novel Transmission Scheduling Based on Deep Reinforcement Learning in Software-Defined Maritime Communication Networks
    Yang, Tingting
    Li, Jiabo
    Feng, Hailong
    Cheng, Nan
    Guan, Wei
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2019, 5 (04) : 1155 - 1166
  • [6] Deep Reinforcement Learning-Based Traffic Sampling for Multiple Traffic Analyzers on Software-Defined Networks
    Kim, Sunghwan
    Yoon, Seunghyun
    Lim, Hyuk
    IEEE ACCESS, 2021, 9 : 47815 - 47827
  • [7] Software-defined networking QoS optimization based on deep reinforcement learning
    Lan J.
    Zhang X.
    Hu Y.
    Sun P.
    Tongxin Xuebao/Journal on Communications, 2019, 40 (12): : 60 - 67
  • [8] A Routing Optimization Method for Software-Defined SGIN based on Deep Reinforcement Learning
    Tu, Zhe
    Zhou, Huachun
    Li, Kun
    Li, Guanglei
    Shen, Qi
    2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [9] Hierarchical Deep Reinforcement Learning-based Load Balancing Algorithm for Multi-domain Software-Defined Networks
    Kolakowski, Robert
    Kuklinski, Slawomir
    Tomaszewski, Lechoslaw
    2024 23RD IFIP NETWORKING CONFERENCE, IFIP NETWORKING 2024, 2024, : 607 - 612
  • [10] Software-Defined Heterogeneous Edge Computing Network Resource Scheduling Based on Reinforcement Learning
    Li, Yaofang
    Wu, Bin
    APPLIED SCIENCES-BASEL, 2023, 13 (01):