Extreme risks associated with natural and man-made disasters involve disruptions in the production of goods or provision of services in interdependent systems. The reduced supply of critical goods and services will degrade "as planned" production outputs and create ripple effects of direct and indirect disruptions. Input-output modeling evaluates the propagation of disaster consequences by quantifying the associated economic risks of disruption, namely economic loss and inoperability, for multi-sectoral economic regions. The thesis enhances the reliability of these risk estimates by formulating a stochastic inventory-based risk assessment model using a multi-objective optimization framework for minimizing (i) economic losses, and (ii) sector inoperability. The research utilizes inventory-to-sales ratio data from the Bureau of Economic Analysis for modeling uncertainty in the levels of finished goods inventory and the beta distribution to integrate uncertainty in decision-maker preferences associated with the multi-objective framework. The framework focuses on the development of a holistic, flexible and scalable decision support system through a Dynamic Cross Prioritization Plot (DCPP) for identifying inventory enhancement opportunities among critically disrupted systems that is applicable to different regions and disaster scenarios.
|Advisor:||Santos, Joost R.|
|Commitee:||Deason, John, Gallay, David, Lambert, James, van Dorp, Johan Rene|
|School:||The George Washington University|
|Department:||Engineering Mgt and Systems Engineering|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 74/07(E), Dissertation Abstracts International|
|Subjects:||Engineering, Systems science, Operations research|
|Keywords:||Disaster risk analysis, Disruption, Input-output analysis, Multi-criteria decision-making, Simulation, Stochastic inventory model|
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