Profit Maximizing Smart Manufacturing Over AI-Enabled Configurable Blockchains

被引:6
|
作者
Teng, Yinglei [1 ]
Li, Lanlin [1 ]
Song, Luona [2 ]
Yu, F. Richard [3 ]
Leung, Victor C. M. [4 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing Key Lab Spaceground Interconnect & Conver, Beijing 100876, Peoples R China
[2] Beijing Informat Sci & Technol Univ, Sch Econ & Management, Beijing 100101, Peoples R China
[3] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
[4] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada
基金
中国国家自然科学基金;
关键词
Blockchain; deep Q network (DQN); Industrial Internet of Things (IIoT); smart manufacturing (SM); INDUSTRIAL INTERNET; CLOUD; OPTIMIZATION; ASSIGNMENT; KNOWLEDGE; SERVICES; PLATFORM; SYSTEMS; THINGS;
D O I
10.1109/JIOT.2021.3098917
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Based on the trustless feature of blockchain, this article designs a general configurable blockchain-enabled smart manufacturing system to achieve flexible manufacturing in response to large-scale manufacturing services. With a transaction pool containing all the pending manufacturing tasks but aligning with the logic flow, the complex manufacturing structure can be uniformly tackled. Furthermore, in virtue of the contradiction between large-scale manufacturing and limited blockchain throughput, we formulate a joint optimization of the block size, task scheduling, and the supply-demand configuration to maximize the customers' net profit with the probabilistic delay requirements, which addresses the critical issue of efficiency and latency in the blockchain-based live manufacturing process. Meanwhile, the production quality and price preference are involved. For solution, a mixed online bipartite matching-based DQN algorithm is proposed, which circumvents the high dimensionality by separating the task-manufacturer matching from the time-correlated problem. Simulation results show that the proposed flexible framework can well adopt to dynamic customer population, and achieves better convergence.
引用
收藏
页码:346 / 358
页数:13
相关论文
共 50 条
  • [41] Introduction to the Special Section on Resiliency for AI-enabled Smart Critical Infrastructures for 5G and Beyond
    Cui, Laizhong
    Wu, Yulei
    Ko, Ryan
    Ladur, Alex
    Wu, Jianping
    ACM TRANSACTIONS ON SENSOR NETWORKS, 2022, 18 (03)
  • [42] An efficient DNN splitting scheme for edge-AI enabled smart manufacturing
    Gauttam, Himanshu
    Pattanaik, K. K.
    Bhadauria, Saumya
    Nain, Garima
    Prakash, Putta Bhanu
    JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2023, 34
  • [43] Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment
    Xia, Le
    Sun, Yao
    Swash, Rafiq
    Mohjazi, Lina
    Zhang, Lei
    Imran, Muhammad Ali
    IEEE NETWORK, 2022, 36 (01): : 197 - 204
  • [44] Climate-smart forestry: an AI-enabled sustainable forest management solution for climate change adaptation and mitigation
    Wang, G. Geoff
    Lu, Deliang
    Gao, Tian
    Zhang, Jinxin
    Sun, Yirong
    Teng, Dexiong
    Yu, Fengyuan
    Zhu, Jiaojun
    JOURNAL OF FORESTRY RESEARCH, 2024, 36 (01)
  • [45] Climate-smart forestry:an AI-enabled sustainable forest management solution for climate change adaptation and mitigation
    GGeoff Wang
    Deliang Lu
    Tian Gao
    Jinxin Zhang
    Yirong Sun
    Dexiong Teng
    Fengyuan Yu
    Jiaojun Zhu
    Journal of Forestry Research, 2025, 36 (01) : 6 - 15
  • [46] Development and application of a human-centric co-creation design method for AI-enabled systems in manufacturing
    Waschull, Sabine
    Emmanouilidis, Christos
    IFAC PAPERSONLINE, 2022, 55 (02): : 516 - 521
  • [47] AI-Enabled Learning Architecture Using Network Traffic Traces over IoT Network: A Comprehensive Review
    Aneja N.
    Aneja S.
    Bhargava B.
    Wireless Communications and Mobile Computing, 2023, 2023
  • [48] IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends
    Qazi, Sameer
    Khawaja, Bilal A.
    Farooq, Qazi Umar
    IEEE ACCESS, 2022, 10 : 21219 - 21235
  • [49] AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory
    Hu, Guoqing
    You, Fengqi
    APPLIED ENERGY, 2024, 356
  • [50] An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture
    Shahid, Muhammad Farrukh
    Khanzada, Tariq J. S.
    Aslam, Muhammad Ahtisham
    Hussain, Shehroz
    Baowidan, Souad Ahmad
    Ashari, Rehab Bahaaddin
    PLANT METHODS, 2024, 20 (01)