Overview: Decision making and design for families of systems (FoS) and systems of systems (SoS) problems is dicult because of complex interactions among member systems, uncertainties in the problem environment, and the presence of multiple decision mak- ers. This dissertation develops a computational framework for decision making in an SoS context based on the techniques of reliability-based multidisciplinary design optimization (RBMDO) and stochastic programming. This approach is chosen be- cause these techniques allow decision makers to model system interactions, propagate uncertainties, and analyze decisions in an integrated method. This dissertation will pursue the following objectives: (1) develop single-loop formulations for performing reliability-based design optimization (RBDO) with component and system reliability constraints and algorithms which are more ecient than standard nonlinear pro- gramming methods for solution, (2) develop methods for performing RBDO with continuous and discrete design and random variables, (3) extend RBDO techniques developed in objectives 1 and 2 to FoS problems; (4) apply RBMDO techniques to SoS problems with cooperative decision makers who delegate decision making to a central authority using systems dynamics modeling of the system of systems and RBMDO methods for system of systems optimization under uncertainty, and (5) apply meth- ods for optimization under uncertainty to SoS problems where there are autonomous decision makers via agent based modeling and stochastic programming.

 

ACKNOWLEDGEMENTS

This study is supported by funds from the National Science Foundation through the Vanderbilt University IGERT program on Risk and Reliability Engineering and partly supported by an Eisenhower Transportation Fellowship from the U.S. Department of Transportation.