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Risk Diffusion and Control under Uncertain Information Based on Hypernetwork

Author

Listed:
  • Ping Yu

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Zhiping Wang

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Yanan Sun

    (School of Science, Dalian Maritime University, Dalian 116026, China)

  • Peiwen Wang

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

Abstract

During the height of the COVID-19 epidemic, production lagged and enterprises could not deliver goods on time, which will bring considerable risks to the supply chain system. Modeling risk diffusion in supply chain networks is important for prediction and control. To study the influence of uncertain information on risk diffusion in a dynamic network, this paper constructs a dynamic evolution model based on a hypernetwork to study risk diffusion and control under uncertain information. First, a dynamic evolution model is constructed to represent the network topology, which includes the addition of links, rewiring of links, entry of nodes, and the exit of outdated nodes that obey the aging principle. Then, the risk diffusion scale is discussed with the Microscopic Markovian Chain Approach (MMCA), and the risk threshold is analyzed. Finally, the consistency of Monte Carlo (MC) simulation and MMCA is verified by MATLAB, and the influence of each parameter on the risk diffusion scale and risk threshold is tested. The results show that reducing the cooperation and production during the risk period, declining the attenuation factor, enhancing the work efficiency of the official media, and increasing the probability of the exit of outdated nodes in the supply chain networks will increase the risk threshold and restrain the risk diffusion.

Suggested Citation

  • Ping Yu & Zhiping Wang & Yanan Sun & Peiwen Wang, 2022. "Risk Diffusion and Control under Uncertain Information Based on Hypernetwork," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4344-:d:977625
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    References listed on IDEAS

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