IDEAS home Printed from https://ideas.repec.org/a/ids/ijcist/v7y2011i3p243-260.html
   My bibliography  Save this article

Infrastructure resilience assessment through control design

Author

Listed:
  • Eric D. Vugrin
  • R. Chris Camphouse

Abstract

Infrastructure resilience is a priority for homeland security in many nations around the globe. This paper describes a new approach for quantitatively assessing the resilience of critical infrastructure systems. The mathematics of optimal control design provides the theoretical foundation for this methodology. This foundation enables the inclusion of recovery costs within the resilience assessment approach, a unique capability for quantitative resilience assessment techniques. This paper describes the formulation of the optimal control problem for a set of representative infrastructure models. This example demonstrates the importance of recovery costs in quantitative resilience analysis, and the increased capability provided by this approach's ability to discern between varying levels of resilience.

Suggested Citation

  • Eric D. Vugrin & R. Chris Camphouse, 2011. "Infrastructure resilience assessment through control design," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 7(3), pages 243-260.
    • Handle: RePEc:ids:ijcist:v:7:y:2011:i:3:p:243-260
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=42994
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mark Turnquist & Eric Vugrin, 2013. "Design for resilience in infrastructure distribution networks," Environment Systems and Decisions, Springer, vol. 33(1), pages 104-120, March.
    2. Yu, Soonyoung & Kim, Sung-Wook & Oh, Chang-Whan & An, Hyunuk & Kim, Jin-Man, 2015. "Quantitative assessment of disaster resilience: An empirical study on the importance of post-disaster recovery costs," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 6-17.
    3. Morshedlou, Nazanin & González, Andrés D. & Barker, Kash, 2018. "Work crew routing problem for infrastructure network restoration," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 66-89.
    4. Geng, Sunyue & Yang, Ming & Mitici, Mihaela & Liu, Sifeng, 2023. "A resilience assessment framework for complex engineered systems using graphical evaluation and review technique (GERT)," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2017. "Energy-economic recovery resilience with Input-Output linear programming models," Energy Economics, Elsevier, vol. 68(C), pages 177-191.
    6. Darayi, Mohamad & Barker, Kash & Nicholson, Charles D., 2019. "A multi-industry economic impact perspective on adaptive capacity planning in a freight transportation network," International Journal of Production Economics, Elsevier, vol. 208(C), pages 356-368.
    7. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    8. Kameshwar, Sabarethinam & Cox, Daniel T. & Barbosa, Andre R. & Farokhnia, Karim & Park, Hyoungsu & Alam, Mohammad S. & van de Lindt, John W., 2019. "Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Jing Song & Weifeng Li, 2019. "Linkage Between the Environment and Individual Resilience to Urban Flooding: A Case Study of Shenzhen, China," IJERPH, MDPI, vol. 16(14), pages 1-15, July.
    10. Eldosouky, AbdelRahman & Saad, Walid & Mandayam, Narayan, 2021. "Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijcist:v:7:y:2011:i:3:p:243-260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=58 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.