Research on the Model of Industrial Interconnection Intelligent Manufacturing Supply and Demand Network and Its Robustness

The industrial interconnection intelligent manufacturing supply and demand network is the carrier for the cooperation of related enterprises, and research on its evolution helps to optimize the cooperation between enterprises. This paper considers the factors that affect enterprise cooperation, appl...

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Bibliographic Details
Main Authors: He, J. (Author), Zheng, T. (Author)
Format: Article
Language:English
Published: Hindawi Limited 2022
Online Access:View Fulltext in Publisher
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Summary:The industrial interconnection intelligent manufacturing supply and demand network is the carrier for the cooperation of related enterprises, and research on its evolution helps to optimize the cooperation between enterprises. This paper considers the factors that affect enterprise cooperation, applies consensus, disconnection, reconnection, and change of opinion into one model, and constructs two models of industrial interconnection intelligent manufacturing supply and demand networks. Because the industrial interconnection intelligent manufacturing supply and demand network is affected by certain factors, the network will be subject to fluctuations and the functions of the network will change. The smaller the function change, the higher the robustness of the model. The related topological structure statistics (such as clustering coefficient, average path length, and degree distribution) of the two models are analyzed, and the analysis results indicate that the network structure of the two models has changed. In addition, different attack algorithms are used to verify the robustness of the model. The experimental results show that under the four different attack algorithms, compared with the BA model, the two models proposed in this paper have stronger robustness. Research on the industrial interconnection of intelligent manufacturing supply and demand networks and its robustness optimization has certain guiding significance. © 2022 Jianjia He and Tianyuan Zheng.
ISBN:10260226 (ISSN)
DOI:10.1155/2022/8076551