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Modeling infrastructure interdependencies by integrating network and fuzzy set theory

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  • Lam, C.Y.
  • Tai, K.

Abstract

Infrastructure interdependency refers to the bidirectional relationship between entities, and means that the state of one entity is influenced by or correlated to the state of the other. Although some interdependencies in infrastructure networks can be modeled deterministically, often the required data are incomplete or there is an element of randomness in the relationships, necessitating the use of stochastic models. In this paper, the concepts and techniques of network and fuzzy set theory are integrated, and a fuzzy modeling approach is proposed to better identify and understand interdependencies and the relationships and connections between entities in infrastructure networks. This approach will allow the topological structures and characteristics of the network to be better understood for further evaluation and analysis.

Suggested Citation

  • Lam, C.Y. & Tai, K., 2018. "Modeling infrastructure interdependencies by integrating network and fuzzy set theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 51-61.
    • Handle: RePEc:eee:ijocip:v:22:y:2018:i:c:p:51-61
    DOI: 10.1016/j.ijcip.2018.05.005
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    References listed on IDEAS

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    Cited by:

    1. Kaul, Hemanshu & Rumpf, Adam, 2022. "A linear input dependence model for interdependent networks," European Journal of Operational Research, Elsevier, vol. 302(2), pages 781-797.
    2. Li, Yulong & Lin, Jie & Zhang, Chi & Zhu, Huaxing & Zeng, Saixing & Sun, Chengshaung, 2022. "Joint optimization of structure and protection of interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    3. Hassan Al-Zarooni & Hamdi Bashir, 0. "An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-23.
    4. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching & Tzeng, Gwo-Hshiung, 2020. "A new soft computing approach for analyzing the influential relationships of critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    5. Lam, C.Y. & Tai, K., 2020. "Network topological approach to modeling accident causations and characteristics: Analysis of railway incidents in Japan," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    6. Hassan Al-Zarooni & Hamdi Bashir, 2020. "An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1204-1226, December.
    7. Zhou, Shenghua & Yang, Yifan & Ng, S. Thomas & Xu, J. Frank & Li, Dezhi, 2020. "Integrating data-driven and physics-based approaches to characterize failures of interdependent infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).

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