Engineered systems often interact with each other and form networks of systems, also referred to as systems of systems. In developing operational policies for these systems, it is important to model the operation of the system of systems, represent and propagate uncertainties through the operational model, and optimize the system under uncertainty. This paper proposes a framework for modeling and optimizing policy decisions for systems of systems under uncertainty in the context of economic policy planning. The proposed framework integrates methods for simulation, uncertainty analysis and optimization under uncertainty. These tools combine to provide decision-makers insights into the impacts of primary and secondary effects resulting from system interdependence. A decoupled approach to optimization under uncertainty is employed using first-order approximations to probability estimates. An example using an economic network illustrates how planners can make more robust decisions under uncertainty using reliability-based optimization methods.

ACKNOWLEDGEMENTS

This study is supported by funds from the National Science Foundation through the Vanderbilt University IGERT program on Risk and Reliability Engineering.