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Fuzzy dynamic input–output inoperability model

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  • Oliva, Gabriele
  • Panzieri, Stefano
  • Setola, Roberto

Abstract

This paper describes an extension of the input–output inoperability model (IIM) that accommodates uncertain and vague data. In the resulting “fuzzy version” of the dynamic IIM model (FD-IIM), the level of inoperability of each infrastructure and the Leontief coefficients are specified using fuzzy variables that express expert knowledge about infrastructure interdependences. An important result is that, under certain conditions, solution convergence for the fuzzy model can be inferred based on the stability properties of the “crisp” (non-fuzzy) version of the system of interest. A case study involving the Italian critical infrastructure is used to demonstrate the feasibility and utility of the approach.

Suggested Citation

  • Oliva, Gabriele & Panzieri, Stefano & Setola, Roberto, 2011. "Fuzzy dynamic input–output inoperability model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 4(3), pages 165-175.
  • Handle: RePEc:eee:ijocip:v:4:y:2011:i:3:p:165-175
    DOI: 10.1016/j.ijcip.2011.09.003
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    References listed on IDEAS

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    1. Setola, Roberto & De Porcellinis, Stefano & Sforna, Marino, 2009. "Critical infrastructure dependency assessment using the input–output inoperability model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 2(4), pages 170-178.
    2. Kenneth G. Crowther & Yacov Y. Haimes & Gideon Taub, 2007. "Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input‐Output Model with Lessons Learned from Hurricane Katrina," Risk Analysis, John Wiley & Sons, vol. 27(5), pages 1345-1364, October.
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    Cited by:

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    2. Heracleous, Constantinos & Kolios, Panayiotis & Panayiotou, Christos G. & Ellinas, Georgios & Polycarpou, Marios M., 2017. "Hybrid systems modeling for critical infrastructures interdependency analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 89-101.
    3. 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.
    4. Skorupski, Jacek & Uchroński, Piotr, 2017. "A fuzzy model for evaluating metal detection equipment at airport security screening checkpoints," International Journal of Critical Infrastructure Protection, Elsevier, vol. 16(C), pages 39-48.
    5. Sina Samimi & Sadoullah Ebrahimnejad & Mohammad Mojtahedi, 2020. "Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 69-88, January.
    6. Olaf Jonkeren & Bogdan Dorneanu & Georgios Giannopoulos & David Ward, 2012. "Regional economic assessment of Critical Infrastructure failure in the EU: A combined systems engineering and economic model," ERSA conference papers ersa12p92, European Regional Science Association.
    7. Amine El Haimar & Joost R. Santos, 2014. "Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 401-415, March.
    8. Niknejad, Ali & Petrovic, Dobrila, 2016. "A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks," International Journal of Production Economics, Elsevier, vol. 179(C), pages 44-58.
    9. Olaf Jonkeren & Ivano Azzini & Luca Galbusera & Stavros Ntalampiras & Georgios Giannopoulos, 2015. "Analysis of Critical Infrastructure Network Failure in the European Union: A Combined Systems Engineering and Economic Model," Networks and Spatial Economics, Springer, vol. 15(2), pages 253-270, June.

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