Vulnerability analysis of Manhattan's motor fuel supply chain network

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.