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Exploring the evolutionary mechanism of complex supply chain systems using evolving hypergraphs

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  • Suo, Qi
  • Guo, Jin-Li
  • Sun, Shiwei
  • Liu, Han

Abstract

A new evolutionary model is proposed to describe the characteristics and evolution pattern of supply chain systems using evolving hypergraphs, in which nodes represent enterprise entities while hyperedges represent the relationships among diverse trades. The nodes arrive at the system in accordance with a Poisson process, with the evolving process incorporating the addition of new nodes, linking of old nodes, and rewiring of links. Grounded in the Poisson process theory and continuum theory, the stationary average hyperdegree distribution is shown to follow a shifted power law (SPL), and the theoretical predictions are consistent with the results of numerical simulations. Testing the impact of parameters on the model yields a positive correlation between hyperdegree and degree. The model also uncovers macro characteristics of the relationships among enterprises due to the microscopic interactions among individuals.

Suggested Citation

  • Suo, Qi & Guo, Jin-Li & Sun, Shiwei & Liu, Han, 2018. "Exploring the evolutionary mechanism of complex supply chain systems using evolving hypergraphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 141-148.
    • Handle: RePEc:eee:phsmap:v:489:y:2018:i:c:p:141-148
    DOI: 10.1016/j.physa.2017.08.002
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    References listed on IDEAS

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    Cited by:

    1. Yu, Ping & Wang, Zhiping & Wang, Peiwen & Yin, Haofei & Wang, Jia, 2022. "Dynamic evolution of shipping network based on hypergraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. 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.
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    4. Zhang, Dezhi & Zhang, Fangtao & Liang, Yijing, 2021. "An evolutionary model of the international logistics network based on the Belt and Road perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    5. Ping Yu & Peiwen Wang & Zhiping Wang & Jia Wang, 2022. "Supply Chain Risk Diffusion Model Considering Multi-Factor Influences under Hypernetwork Vision," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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