Authors: Arash Beheshtian; Kieran Donaghy; Xue Zhang; Rick Geddes
Addresses: Cornell Program in Infrastructure Policy, 2301 Martha Van Rensselaer Hall, Cornell University, Ithaca, NY, 14853, USA ' Department of City and Regional Planning, 312 W. Sibley Hall, Cornell University, Ithaca, NY, 14850, USA ' Department of City and Regional Planning, 312 W. Sibley Hall, Cornell University, Ithaca, NY, 14850, USA ' Department of Policy Analysis and Management, 2301 Martha Van Rensselaer Hall, Cornell University, Ithaca, NY, 14853, USA
Abstract: We extend the concept of a critical infrastructure (CI) network's vulnerability and advance a methodological approach for identifying the vulnerability of a CI extended over a large expanse of space - Manhattan's motor fuel supply chain - in the face of extreme weather events. In the methodological approach, we search for the network's disrupted component(s) having the maximum impact on the spatially extensive network's operability if maintained or repaired. To do so, we developed a bi-stage mixed integer stochastic mathematical program to rank disrupted elements that are the best candidates for fortifying investments. Simulation experiments with the model reveal that its solution identifies a different set of vulnerable components than are identified through the most commonly employed approach. Model results also indicate that a CI network's vulnerability in the face of extreme weather events is highly responsive to network topology in time of disaster and the objective function defined by the modeller.
Keywords: disaster; hurricane; Manhattan flooding; fuel supply chain; vulnerability analysis; climate change; resilience; gas station.
International Journal of Critical Infrastructures, 2019 Vol.15 No.2, pp.163 - 180
Received: 08 Nov 2017
Accepted: 23 Jul 2018
Published online: 04 Feb 2019 *