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The complexity measurement and evolution analysis of supply chain network under disruption risks

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  • Wang, Hua
  • Gu, Tao
  • Jin, Maozhu
  • Zhao, Rong
  • Wang, Guanxiang

Abstract

Based on the study of the complexity of supply chain network, this chapter mainly analyzes the complexity of supply chain network structure in the context of disruption risk, and proposes a quantitative measurement method for the complexity of supply chain network based on DSM model. On this basis, this measure method is used to measure the complexity and evolution of ER random networks, small-world networks, and BA scale-free networks. Finally, the internal relationship between the complexity value, network size and connection probability is analyzed, which provides a theoretical basis for the structural design of supply chain network under the disruption risks.

Suggested Citation

  • Wang, Hua & Gu, Tao & Jin, Maozhu & Zhao, Rong & Wang, Guanxiang, 2018. "The complexity measurement and evolution analysis of supply chain network under disruption risks," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 72-78.
    • Handle: RePEc:eee:chsofr:v:116:y:2018:i:c:p:72-78
    DOI: 10.1016/j.chaos.2018.09.018
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    References listed on IDEAS

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    1. Alessandro Lomi & Philippa Pattison, 2006. "Manufacturing Relations: An Empirical Study of the Organization of Production Across Multiple Networks," Organization Science, INFORMS, vol. 17(3), pages 313-332, June.
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    Cited by:

    1. Qing Zhang & Weiguo Fan & Jianchang Lu & Siqian Wu & Xuechao Wang, 2021. "Research on Dynamic Analysis and Mitigation Strategies of Supply Chains under Different Disruption Risks," Sustainability, MDPI, vol. 13(5), pages 1-29, February.
    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. Silvia Carpitella & Ilyas Mzougui & Joaquín Izquierdo, 2022. "Multi-criteria risk classification to enhance complex supply networks performance," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 769-785, September.
    4. Satyendra Kumar Sharma & Praveen Ranjan Srivastava & Ajay Kumar & Anil Jindal & Shivam Gupta, 2023. "Supply chain vulnerability assessment for manufacturing industry," Annals of Operations Research, Springer, vol. 326(2), pages 653-683, July.
    5. Zhang, Dezhi & Zhang, Fangtao & Liang, Yijing, 2021. "An evolutionary model of the international logistics network based on the Belt and Road perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Wang, Jiepeng & Zhou, Hong & Jin, Xiaodan, 2021. "Risk transmission in complex supply chain network with multi-drivers," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).

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