Quantifying restoration costs in the aftermath of an extreme event using system dynamics and dynamic mathematical modeling approaches

"Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The first research contribution is a system dynamics inoperability model that determines inputs, outputs, and flows for roadway networks. This model can be used to identify the connectivity of road segments and better understand how inoperability contributes to economic consequences. The second contribution is an algorithm that integrates critical infrastructure data derived from bottom-up cost estimation technique as part of an object-oriented software tool that can be used to determine the impact of system disruptions. The third contribution is a dynamic mathematical model that establishes a framework to estimate post-disaster restoration costs from a whole system perspective. Engineering managers, city planners, and policy makers can use the methodologies developed in this research to develop effective disaster planning schemas and to prioritize post-disaster restoration operations"--Abstract, page iv.

Name thesis:degree_name

Ph. D. in Engineering Management

Grantor

Missouri University of Science and Technology

Author dc:creator

• Ojha, Akhilesh

• Critical infrastructure
• Resource estimation
• Restoration
• Supply chain
• Transportation network
• Operations Research, Systems Engineering and Industrial Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2788

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3793