Date of Award

3-22-2012

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Systems Engineering and Management

First Advisor

William E. Sitzabee, PhD.

Abstract

A risk analysis methodology is necessary to manage the potential effects of oil refinery outages to the increasingly connected, interdependent critical infrastructure of the United States. Following the terrorist attacks of 9/11, the lack of a critical infrastructure risk mitigation strategy was identified as an area for improvement. In both the 9/11 attacks and Hurricane Katrina, cascading failures occurred due to the interdependencies among infrastructures and their spatial relationships. Furthermore, the U.S. military is dependent on oil refining capability and a major shortage could potentially have devastating effects on mission accomplishment. As a result, a need has emerged to better quantify the risks associated with disasters to critical infrastructure within the United States. Currently, the Department of Homeland Security's risk equation only measures the individual risks associated with the individual parts of an infrastructure system; it does not measure impacts to the entire system. The goal of this study is to establish a process and develop techniques to account for risk to both the critical infrastructure system and the critical components of the system. The study proposes a modified risk equation that incorporates the traditional elements of individual risk and the system elements of risk. The modified equation proposes two additional variables: Spatial Relationship and Coupling Effect. Three scholarly articles are presented to describe the development of these variables and to compare the traditional and modified risk equations. The modified equation has three benefits: the system effects are incorporated into the current equation, the equation provides more fidelity and minimizes additional data, and the additional data is easily executed.

AFIT Designator

AFIT-GEM-ENV-12-M18

DTIC Accession Number

ADA557948

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Risk Analysis Commons

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