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Outer synchronization and parameter identification approach to the resilient recovery of supply network with uncertainty

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  • Geng, Liang
  • Xiao, Renbin

Abstract

The resilient recovery is of great necessity and importance to supply network with uncertainty. Outer synchronization helps the supply network restores the scheduled status. Also it has absorptive and adaptive capacity. This paper proposes a resilient recovery method based on outer synchronization. Different from previous schemes, a research on supply networks with time delay, unknown parameters and structural variation has been conducted. A dynamic model of the supply network is established, in which each node is represented as a Lorenz system. Based on the Lyapunov stability theory, an impulsive pinning controller is designed for resilient recovery. Numerical simulations and related case study are used to verify the validity of the proposed model and the resilient recovery method. Experimental results show that, the proposed method can realize the parameter identification and effective recovery with various uncertain environments.

Suggested Citation

  • Geng, Liang & Xiao, Renbin, 2017. "Outer synchronization and parameter identification approach to the resilient recovery of supply network with uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 407-421.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:407-421
    DOI: 10.1016/j.physa.2017.04.025
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    Cited by:

    1. Yanjie Ji & Zhaoyan Wu, 2021. "Outer Synchronization of Complex-Variable Networks with Complex Coupling via Impulsive Pinning Control," Mathematics, MDPI, vol. 9(17), pages 1-10, September.
    2. Chai, Yuan & Xu, Li & Zhang, Hudong, 2021. "Interchangeable outer synchronization of community networks with two spatiotemporal clusters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    3. Fu, Xiuwen & Xu, Xiaojie & Li, Wenfeng, 2024. "Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    4. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

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