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Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing

by Yanfei Sun1, Feng Qiao2, Wei Wang1, Bin Xu1, Jianming Zhu1, Romany Fouad Mansour3, Jin Qi1,*

1 School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
2 College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
3 Department of Mathematics, Faculty of Science, New Valley University, El-Karaga, 72511, Egypt

* Corresponding Author: Jin Qi. Email: email

Computers, Materials & Continua 2022, 72(2), 2825-2843. https://doi.org/10.32604/cmc.2022.026574

Abstract

As a new mode and means of smart manufacturing, smart cloud manufacturing (SCM) faces great challenges in massive supply and demand, dynamic resource collaboration and intelligent adaptation. To address the problem, this paper proposes an SCM-oriented dynamic supply-demand (S-D) intelligent adaptation model for massive manufacturing services. In this model, a collaborative network model is established based on the properties of both the supply-demand and their relationships; in addition, an algorithm based on deep graph clustering (DGC) and aligned sampling (AS) is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model. At the same time, an intelligent supply-demand adaptation method driven by the quality of service (QoS) is established, in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning (DRL) powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty. The results show that the model and the solution proposed in this paper can perform collaborative and intelligent supply-demand adaptation for the massive and dynamic resources in SCM through autonomous learning and can effectively perform global supply-demand matching and optimal resource allocation.

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APA Style
Sun, Y., Qiao, F., Wang, W., Xu, B., Zhu, J. et al. (2022). Dynamic intelligent supply-demand adaptation model towards intelligent cloud manufacturing. Computers, Materials & Continua, 72(2), 2825-2843. https://doi.org/10.32604/cmc.2022.026574
Vancouver Style
Sun Y, Qiao F, Wang W, Xu B, Zhu J, Fouad Mansour R, et al. Dynamic intelligent supply-demand adaptation model towards intelligent cloud manufacturing. Comput Mater Contin. 2022;72(2):2825-2843 https://doi.org/10.32604/cmc.2022.026574
IEEE Style
Y. Sun et al., “Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing,” Comput. Mater. Contin., vol. 72, no. 2, pp. 2825-2843, 2022. https://doi.org/10.32604/cmc.2022.026574



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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