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Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability

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  • Tinggui Chen

    (School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310008, China
    School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310008, China)

  • Shiwen Wu

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310008, China)

  • Jianjun Yang

    (Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA 30566, USA)

  • Guodong Cong

    (School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310008, China)

Abstract

Emergency logistics plays an important role in the rescue process after sudden disasters. However, in the process of emergency logistics activities, risks may arise due to scheduling problems or insufficient supply of warehouse stocks, resulting in an insufficient rescue capacity. In addition, the risk of emergency logistics is random and may exist in a certain link or throughout the whole rescue process of emergency logistics. Consequently, the disaster site may be invaded by sudden disaster risk due to the lack of necessary material supplies. The entire emergency logistics system may be destroyed and cause even greater losses as well. Based on this phenomenon, this paper introduces reliability factors of materials and combines the complex network theory to build an emergency logistics network and analyze the emergency logistics risk propagation mechanism. This paper firstly builds an emergency logistics network based on complex network theory. Then, it combines the improved epidemic model to analyze the influencing factors of risk propagation in the emergency logistics network. Finally, this paper probes into the emergency logistics risk propagation mechanisms and processes in terms of network type, material reliability, rescue speed, etc. Furthermore, this paper identifies key factors for risk control and proposes countermeasures to further spread risks, thereby reducing the risk to loss of economic life.

Suggested Citation

  • Tinggui Chen & Shiwen Wu & Jianjun Yang & Guodong Cong, 2019. "Risk Propagation Model and Its Simulation of Emergency Logistics Network Based on Material Reliability," IJERPH, MDPI, vol. 16(23), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4677-:d:290280
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    References listed on IDEAS

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    Cited by:

    1. Peihua Fu & Bailu Jing & Tinggui Chen & Jianjun Yang & Guodong Cong, 2020. "Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions," IJERPH, MDPI, vol. 17(18), pages 1-29, September.
    2. Tinggui Chen & Qianqian Li & Peihua Fu & Jianjun Yang & Chonghuan Xu & Guodong Cong & Gongfa Li, 2020. "Public Opinion Polarization by Individual Revenue from the Social Preference Theory," IJERPH, MDPI, vol. 17(3), pages 1-29, February.
    3. Li, Junjun & Yu, Anqi & Xu, Bowei, 2022. "Risk propagation and evolution analysis of multi-level handlings at automated terminals based on double-layer dynamic network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    4. Alptekin Ulutaş & Ieva Meidute-Kavaliauskiene & Ayse Topal & Ezgi Demir, 2021. "Assessment of Collaboration-Based and Non-Collaboration-Based Logistics Risks with Plithogenic SWARA Method," Logistics, MDPI, vol. 5(4), pages 1-14, November.
    5. Ziyuan Liu & Zhi Li & Weiming Chen & Yunpu Zhao & Hanxun Yue & Zhenzhen Wu, 2020. "Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm," IJERPH, MDPI, vol. 17(16), pages 1-18, August.

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