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<span style="font-size:20.0pt;font-family:"Tahoma",sans-serif;
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color:#222222">The Industrial Engineering Department</span><span style="mso-fareast-font-family:"Times New Roman""></span></h2>
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color:#222222">Invites the Cullen College of Engineering</span><span style="mso-fareast-font-family:"Times New Roman""></span></h2>
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color:#222222">To the</span><span style="mso-fareast-font-family:"Times New Roman""></span></h2>
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<span style="font-size:16.0pt;font-family:"Tahoma",sans-serif;
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color:#222222">PhD Dissertation Defense</span><span style="mso-fareast-font-family:
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<b><span style="font-size:14.0pt;font-family:"Tahoma",sans-serif;mso-bidi-font-family:
Arial;color:#222222">Optimizing Infrastructure Resilience under Budgetary Constraint</span></b><span style="font-family:"Calibri",sans-serif"></span></p>
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<b><span style="font-size:14.0pt;font-family:"Tahoma",sans-serif;mso-bidi-font-family:
Arial;color:#222222">Mohammad Najarian</span></b><span style="font-family:"Calibri",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif;color:#1F497D"> </span></b><b><span style="font-family:"Arial",sans-serif;color:#222222">Date: Monday, December 2, 2019 </span></b><span style="font-family:"Calibri",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif;color:#222222">Location: Dean's Conference Room
</span></b><span style="font-family:"Arial",sans-serif;
color:#222222">(E421H in Engineering Building 2)</span><span style="font-family:
"Calibri",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif;color:#222222">Time: </span></b><span style="font-family:"Arial",sans-serif;color:#222222">10:30 AM – 12:00 PM</span><span style="font-family:"Calibri",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif;color:#222222">Committee Chair:</span></b><b><span style="font-family:"Arial",sans-serif;color:#1F497D"> </span></b><span style="font-family:"Arial",sans-serif;color:#222222">Dr. Gino Lim</span><span style="font-family:"Calibri",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif;color:#222222">Committee Members:</span></b><span style="font-family:"Calibri",sans-serif"></span></p>
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<span style="font-family:"Arial",sans-serif;color:#222222">Dr. Qianmei Feng, Dr. Ying Lin, Dr. Cumaraswamy Vipulanandan, Dr. Yunpeng Zhang</span><span style="font-family:"Calibri",sans-serif"></span></p>
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<span style="font-size:
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color:#222222">Abstract</span><a></a><span style="mso-fareast-font-family:"Times New Roman""></span></h1>
<p style="margin-right: 0in; margin-left: 0in; font-size: 12pt; font-family: "Times New Roman", serif">
Natural and manmade disasters are low-probability high-impact adverse events that incur extravagant cost and hardship on society. One way to mitigate the impact of these unfavorable events is to enhance the resilience of the system. Resilience is defined as
the ability of a system to reduce the impact of an event and return it to its initial state in minimal time. The primary objective of this study is to develop methods for enhancing system resilience. To do so, this study addresses the following questions:
How can we measure resilience? Is a given resilience metric suitable for our system? How can we allocate a restricted budget to the components to maximize improvement of the resilience? And finally, if we have limited budget to select a limited number of components
whose failure has the highest impact on the system, what are those components?</p>
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This dissertation work begins with building a resilience metric upon conceptual frameworks. Then a new methodology has been developed based on statistical methods to assess the quality of resilience metrics. Utility curves are introduced to connect alternative
component enhancement options with associated cost and effect on the component functionality. A new concept, resilience-based component importance, is presented to map the component’s functionality onto the system’s functionality. Finally, we developed a mathematical
programming model to maximize the resiliency of the system by allocating a limited budget to the system’s components. In the mathematical model, a discretization approach along with utility curves and resilience-based component importance has been developed
to reduce the complexity of the system for solving the optimization model. This approach provides decision makers with insight into how much budget to allocate to each component and what aspects of the component must be enhanced.</p>
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