[CCoE Notice] PhD Dissertation Defense

[Knudsen, Rachel W]

The Industrial Engineering Department

Invites the Cullen College of Engineering
To the
PhD Dissertation Defense

Optimizing Infrastructure Resilience under Budgetary Constraint

Mohammad Najarian

 Date: Monday, December 2, 2019

Location: Dean's Conference Room (E421H in Engineering Building 2)

Time: 10:30 AM – 12:00 PM

Committee Chair: Dr. Gino Lim

Committee Members:

Dr. Qianmei Feng, Dr. Ying Lin, Dr. Cumaraswamy Vipulanandan, Dr. Yunpeng Zhang

Abstract

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?

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.




<mailto:riward at central.uh.edu>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20191120/a2dbff1e/attachment-0001.html