| Failure of infrastructure elements due to an extreme event adversely affects the supply chain networks in any urban environment. This study models the resiliency of a supply chain network by integrating geospatial data with critical supply chain elements and graph theory. This model can then be combined with an extreme event simulation to calculate resiliency. Resiliency refers to the time required for services and select economic indicators to approach pre-event levels (in this study, a return to 80% of the pre-event capacity). This research proposes an interdisciplinary approach to develop a comprehensive framework for resiliency modeling. This approach includes the integration of: graph theory, geospatial data, supply chain assessment, and hazards risk analysis. A geographic information system (GIS) is constructed from data extracted or derived from The National Map of the U.S. Geological Survey for Johnson County, Kansas in the Greater Kansas City area. These data are used to generate a combinatorial graph from which resiliency is calculated in the aftermath of a simulated EF-5 tornado. The graph shows the interconnectivity between the different supply chain elements, as well as the inter-dependability among them. Reconstruction in the aftermath of the tornado simulation is manipulated to promote a rapid recovery of the supply chain network. These results are then compared with ground truth data for the EF-5 tornado that devastated Joplin, Missouri on 22 May 2011. Preliminary results show a good level of agreement between the constructed model and real-world events. |
