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Fuzzy Emergency Model and Robust Emergency Strategy of Supply Chain System under Random Supply Disruptions

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  • Songtao Zhang
  • Panpan Zhang
  • Min Zhang

Abstract

For random distributors under supply disruptions caused by emergency incidents, a fuzzy emergency model and a robust emergency strategy of the supply chain system are studied. First, for a kind of supply chain system composed of a strategic manufacturer, a backup manufacturer, and multiple distributors, the basic emergency models, including the inventory models and a total cost model, are constructed under random supply disruptions. Then, based on the Takagi-Sugeno fuzzy system, the basic emergency models of the supply chain system are converted into a discrete switching model, which can realize soft switching among the basic emergency models. Furthermore, according to the different inventory levels, the strategic manufacturer’s production strategies and the distributors’ ordering strategies are designed to reduce the inventory costs of the node enterprises in supply chain system. Second, by defining a discrete piecewise Lyapunov function in each maximal overlapped-rules group, a new fuzzy robust emergency strategy for the supply chain system is proposed through the principle of parallel distributed compensation. This emergency strategy can not only restore the impaired supply chain to the normal operation state but also keep the total cost of the supply chain at a low level and guarantee the robust stability of the emergency supply chain system. Finally, the simulation results illustrate the effectiveness of the proposed fuzzy robust emergency strategy of the supply chain system.

Suggested Citation

  • Songtao Zhang & Panpan Zhang & Min Zhang, 2019. "Fuzzy Emergency Model and Robust Emergency Strategy of Supply Chain System under Random Supply Disruptions," Complexity, Hindawi, vol. 2019, pages 1-10, January.
  • Handle: RePEc:hin:complx:3092514
    DOI: 10.1155/2019/3092514
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    References listed on IDEAS

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    1. Jing Hou & Lindu Zhao, 2012. "Backup agreements with penalty scheme under supply disruptions," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(5), pages 987-996.
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    4. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    5. Xuejie Bai & Yankui Liu, 2016. "Robust optimization of supply chain network design in fuzzy decision system," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1131-1149, December.
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    Cited by:

    1. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    2. Jianhua Chen & Ting Yin, 2023. "Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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