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Models for restoration decision making for a supply chain network after a cyber attack

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  • Emily A Heath
  • John E Mitchell
  • Thomas C Sharkey

Abstract

This work considers modeling approaches to the problem of restoration decision making by supply chain managers. Specifically, we consider an oil supply chain network that has suffered a cyber attack. As a result of the attack, critical services to the supply chain are lost, and the manager has uncertain information regarding when these services will be available. We first consider a deterministic model where the supply chain manager has complete information about the restoration of damaged services. We extend this model to a stochastic programming model involving several different recovery scenarios. We present computational results for a realistic case study developed with an extensive literature survey. We compare results of the stochastic model with average deterministic results, and show that the average deterministic results do not produce good solutions for an uncertain setting. We conclude with general remarks on how supply chain managers should consider restoration decisions under uncertainty.

Suggested Citation

  • Emily A Heath & John E Mitchell & Thomas C Sharkey, 2020. "Models for restoration decision making for a supply chain network after a cyber attack," The Journal of Defense Modeling and Simulation, , vol. 17(1), pages 5-19, January.
    • Handle: RePEc:sae:joudef:v:17:y:2020:i:1:p:5-19
    DOI: 10.1177/1548512918808410
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    References listed on IDEAS

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    1. Nurre, Sarah G. & Cavdaroglu, Burak & Mitchell, John E. & Sharkey, Thomas C. & Wallace, William A., 2012. "Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem," European Journal of Operational Research, Elsevier, vol. 223(3), pages 794-806.
    2. Li, Xiaoping & Batta, Rajan & Kwon, Changhyun, 2017. "Effective and equitable supply of gasoline to impacted areas in the aftermath of a natural disaster," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 25-34.
    3. Anantaram Balakrishnan & Thomas L. Magnanti & Joel S. Sokol & Yi Wang, 2002. "Spare-Capacity Assignment For Line Restoration Using a Single-Facility Type," Operations Research, INFORMS, vol. 50(4), pages 617-635, August.
    4. Anantaram Balakrishnan & Thomas Magnanti & Joel Sokol & Yi Wang, 2001. "Telecommunication Link Restoration Planning with Multiple Facility Types," Annals of Operations Research, Springer, vol. 106(1), pages 127-154, September.
    5. Ni, Ni & Howell, Brendan J. & Sharkey, Thomas C., 2018. "Modeling the impact of unmet demand in supply chain resiliency planning," Omega, Elsevier, vol. 81(C), pages 1-16.
    6. Eric D. Vugrin & Mark A. Turnquist & Nathanael J.K. Brown, 2014. "Optimal recovery sequencing for enhanced resilience and service restoration in transportation networks," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 10(3/4), pages 218-246.
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    Cited by:

    1. Cheung, Kam-Fung & Bell, Michael G.H. & Bhattacharjya, Jyotirmoyee, 2021. "Cybersecurity in logistics and supply chain management: An overview and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).

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