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An ant colony based resilience approach to cascading failures in cluster supply network

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  • Wang, Yingcong
  • Xiao, Renbin

Abstract

Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant’s spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony’s social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

Suggested Citation

  • Wang, Yingcong & Xiao, Renbin, 2016. "An ant colony based resilience approach to cascading failures in cluster supply network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 150-166.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:150-166
    DOI: 10.1016/j.physa.2016.06.058
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    Cited by:

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    2. Mu, Dong & Ren, Huanyu & Wang, Chao & Yue, Xiongping & Du, Jianbang & Ghadimi, Pezhman, 2023. "Structural characteristics and disruption ripple effect in a meso-level electric vehicle Lithium-ion battery supply chain network," Resources Policy, Elsevier, vol. 80(C).
    3. Su, Jia & Huang, Guangqiu, 2018. "Simulation and analysis of ecosystem vulnerability with cascading spread caused by dust migration based on object function GeoPetri net," Ecological Modelling, Elsevier, vol. 379(C), pages 54-72.
    4. Li, Jie & Wang, Ying & Zhong, Jilong & Sun, Yun & Guo, Zhijun & Chen, Zhiwei & Fu, Chaoqi, 2022. "Network resilience assessment and reinforcement strategy against cascading failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. 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).
    6. Zhang, Chao & Xu, Xin & Dui, Hongyan, 2020. "Resilience Measure of Network Systems by Node and Edge Indicators," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. 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).
    8. 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.

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