EAGER: SSDIM: Generating Synthetic Data on Interdependent Food, Energy, and Transportation Networks via Stochastic, Bi-level Optimization

Unanticipated challenges, such as natural disasters, oil spils, blackouts, result not only in disruptions to the respective engineering systems but also lead to a range of complex social effects. Therefore, the study of such events requires a framework that integrates engineering with the social, behavioral, and economic sciences. A range of planning methods have been traditionally employed to enhance decision making, however, they often lack data at the level of detail required for resiliency studies.

The goal of this project is to generate data at the interdependencies between the food and the energy systems, at the state level. Faster computation and improved algorithms allow us to generate state level data for the individual large-scale subsystems via a Bi-level Optimization Program that utilizes existing, publicly available data on food and energy systems. Data at the interdependencies of the subsystems are generated using inputs from the Economic Input-Output Life Cycle Analysis tool. The aim of this project is to understand the critical inter-dependencies between infrastructures and consequently inform strategies for disaster preparedness, resilience, and response.