Open Access

ARTICLE

Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing

Yanfei Sun¹, Feng Qiao², Wei Wang¹, Bin Xu¹, Jianming Zhu¹, Romany Fouad Mansour³, Jin Qi^1,*

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:

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

Received 30 December 2021; Accepted 02 March 2022; Issue published 29 March 2022

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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Keywords

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