Theses and Dissertations

ORCID

https://orcid.org/0000-0001-8125-435X

Issuing Body

Mississippi State University

Advisor

Tajik, Nazanin

Committee Member

Young, Max

Committee Member

Marufuzzaman, Mohammad

Committee Member

Wang, Haifeng

Date of Degree

12-13-2024

Original embargo terms

Worldwide

Document Type

Dissertation - Open Access

Major

Industrial and System Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

Despite much hope for climate change to slow down or even reverse, younger generations face a future overshadowed by extreme events. The indisputable reality is that unless the United Nations establishes comprehensive and sustained climate justice policies, children today will experience five times more extreme events than those that took place a century ago. On Monday, July 3rd of 2023, an unprecedented peak in global temperatures was documented, marking the highest global temperature ever recorded, as the U.S. National Centers for Environmental Prediction reported. These increasing temperatures indicate the ongoing and intensifying phenomenon of climate change, which amplifies the frequency and severity of certain natural disasters. Given that vulnerability reflects the extent of damage following a disruptive event, reducing vulnerability is a critical initial step toward enhancing resilience—the capacity to withstand and recover from such disruptions. Reflecting on the words of H. James Harrington, the seminal figure in organizational performance improvement, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” The findings of this study highlight a macroscopic approach to understanding and predicting network vulnerability in the face of uncertain disruptive events by focusing on the statistical analysis of global measures (GMs) related to network topological characteristics. The distribution of GM values across 15 pure network topologies reveals specific patterns. This discovery offers a novel metric for assessing the performance of networks with unknown topologies by comparing their GM patterns to those of the studied topologies. Furthermore, by intertwining local vulnerability assessments with our scenario-based strategy, we aim to conduct a thorough examination of each node’s significance in maintaining network integrity during disruptions. This analysis is intended to uncover the underlying structural intricacies of these networks, enabling a comparison with established topological standards to identify opportunities for optimization. Additionally, we expand the scope of our model by incorporating traffic flow considerations using the Bureau of Public Roads (BPR) function to optimize network resilience. Key words: Global Measures, Vulnerability, Uncertainty in vulnerability, Connectivity, Accessibility, Criticality, Network topology, Local Measures, Bureau of Public Roads (BPR), Scenario based-Two stage stochastic programming, Risk

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