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Agri-food supply chain network disruption propagation and recovery based on cascading failure

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  • Li, Zhuyue
  • Zhao, Peixin
  • Han, Xue

Abstract

In recent years, some extreme weather and public health events have led to frequent disruptions in agri-food supply chain networks in China, and scholars recognize the importance of this problem, but there are relatively few studies on the disruption propagation. Firstly, we construct the agri-food supply chain networks and the weak tie networks, and innovatively introduce weak ties into the disruption propagation of the agri-food supply chain networks. Secondly, we analyze the impact of two strategies, strengthening existing business relationships and establishing new business relationships, on the disruption propagation of agri-food supply chain networks under extreme natural disasters or unexpected wholesale market closures. Based on the results, the impact of disruption recovery on supply–demand relationships in agri-food supply chain network is analyzed. This research provides decision-making reference against supply chain disruptions and related problems.

Suggested Citation

  • Li, Zhuyue & Zhao, Peixin & Han, Xue, 2022. "Agri-food supply chain network disruption propagation and recovery based on cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
  • Handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121008712
    DOI: 10.1016/j.physa.2021.126611
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    1. Duan, Dong-Li & Ling, Xiao-Dong & Wu, Xiao-Yue & OuYang, Di-Hua & Zhong, Bin, 2014. "Critical thresholds for scale-free networks against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 252-258.
    2. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
    3. Erfan Babaee Tirkolaee & Zahra Dashtian & Gerhard-Wilhelm Weber & Hana Tomaskova & Mehdi Soltani & Nasim Sadat Mousavi, 2021. "An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness," Mathematics, MDPI, vol. 9(11), pages 1-30, June.
    4. Kevin P. Scheibe & Jennifer Blackhurst, 2018. "Supply chain disruption propagation: a systemic risk and normal accident theory perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 43-59, January.
    5. Youyu Chen & Tong Shu & Shou Chen & Shouyang Wang & Kin Keung Lai & Lu Gan, 2017. "Strong–weak collaborative management in coping supply chain disruption risk transmission based on scale-free networks," Applied Economics, Taylor & Francis Journals, vol. 49(39), pages 3943-3958, August.
    6. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    7. Emel Savku & Gerhard-Wilhelm Weber, 2018. "A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 696-721, November.
    8. Ledwoch, Anna & Yasarcan, Hakan & Brintrup, Alexandra, 2018. "The moderating impact of supply network topology on the effectiveness of risk management," International Journal of Production Economics, Elsevier, vol. 197(C), pages 13-26.
    9. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    10. Juan Yang & Haorui Liu, 2018. "Research of Vulnerability for Fresh Agricultural-Food Supply Chain Based on Bayesian Network," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-17, December.
    11. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    12. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    13. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    14. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    15. Busra Zeynep Temocin & Ralf Korn & A. Sevtap Selcuk-Kestel, 2018. "Constant proportion portfolio insurance in defined contribution pension plan management under discrete-time trading," Annals of Operations Research, Springer, vol. 260(1), pages 515-544, January.
    16. Catherine Brinkley, 2018. "The Small World of the Alternative Food Network," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    17. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Complex interdependent supply chain networks: Cascading failure and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 58-69.
    18. Armin Jabbarzadeh & Behnam Fahimnia & Fatemeh Sabouhi, 2018. "Resilient and sustainable supply chain design: sustainability analysis under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5945-5968, September.
    19. Busra Zeynep Temocin & Ralf Korn & A. Sevtap Selcuk-Kestel, 2018. "Constant proportion portfolio insurance in defined contribution pension plan management," Annals of Operations Research, Springer, vol. 266(1), pages 329-348, July.
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