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Situational awareness of the operational status of specific, critical supply and demand nodes following a major disaster may inform response and recovery activities based on the ability of an infrastructure asset or system to support core facility operations. Near-real-time analysis of infrastructure dependency information is a computationally intensive process that has generally been observed informally by public safety officials. While system-level information may be desired, it has been beyond the capabilities of most local public safety and emergency management agencies. To address this problem, a Grass-roots Infrastructure Dependency Model (GRID-M) was developed to enable near-real-time analysis of physical infrastructure dependencies of specific supply and demand nodes within four lifeline sectors: electricity, natural gas, water, and wastewater. The operational status of each node can be characterized as operational, partially operational, or not operational. These statuses are obtained by matching real-time outage or disruption data from utility providers with predetermined specific coping strategies based on a preincident limited infrastructure survey for specific assets within a network. This information can also be paired with a limited damage assessment to provide awareness of the accessibility to, and physical state of, each node within supply chains of interest. GRID-M displays all outputs within a Geographic Information Systems environment with additional prepopulated layers such as real-time traffic and demographic information of the affected communities. As such, GRID-M may be used following a major disaster to support the identification of priority response and recovery objectives based on the disruptions of critical local supply chains and their relationship with affected communities.
critical infrastructure, dependency, disaster, emergency management, preparedness, resilience, supply chain
[1] Gaspar, J., Kolari, J., Hise, R., Bierman, L. & Smith, L.M., Introduction to Global Business: Understanding the International Environment & Global Business Functions, Cengage Learning: Boston, p. 329, 2017.
[2] Vakharia, A.J. & Yenipazarli, A., Managing supply chain disruptions. Foundations and Trends in Technology, Information and Operations Management, 2(4), pp. 245–255, 2009.
[3] Kleindorfer, P.R. & Saad, G.H., Managing disruption risks in supply chains. Production and Operations Management, 14(1), pp. 53–68, 2005. https://doi.org/10.1111/j.1937-5956.2005.tb00009.x
[4] Hurricane Sandy After-Action Report, United States Department of Homeland Security, Federal Emergency Management Agency, available at https://www.fema.gov/medialibrary-data/20130726-1923-25045-7442/sandy_fema_aar.pdf (accessed 30 January 2017)
[5] National Infrastructure Protection Plan 2013: Partnering for Critical Infrastructure Security and Resilience, United States Department of Homeland Security, available at https://www.dhs.gov/publication/nipp-2013-partnering-critical-infrastructure-securityand-resilience(accessed 30 January 2017)
[6] Presidential Policy Directive—Critical Infrastructure Security and Resilience, The White House, available at https://obamawhitehouse.archives.gov/the-press-office/2013/02/12/ presidential-policy-directive-critical-infrastructure-security-and-resil. (accessed 30 January 2017)
[7] Rinaldi, S., Peerenboom, J. & Kelly, T., Identifying, understanding and analyzing critical infrastructure interdependencies. IEEE Control Systems, 21(6), pp. 11–25, 2001. https://doi.org/10.1109/37.969131 [8] Allen, D.W., Getting to Know ArcGIS Modelbuilder, Esri Press, Redlands, California: 2011.