Problems of increasing complexity are facing decision-makers within today's government and industry, and the moniker of "system-of-systems" is increasingly being applied to many of these challenges. With multiple, evolving, heterogeneous, distributed systems involved and embedded in networks at multiple levels, a guiding methodological framework is needed to enable adequate decision-support. A holistic framework is sought to enable decision makers to discern whether related infrastructure, operational, policy, economic, and/or technology considerations together will be effective, ineffective, or indifferent over time. The need to address these problems as systems-of-systems is urgent and critical, because they involve decisions that commit large amounts of money and resources, for which ultimate failure or success carries heavy consequences for society now and for many future generations.
The mission statement for the on-going research in the System-of-Systems Laboratory can be summarized as: generation of system-of-systems formulations/tools/processes to understand and bound complex problems and create an ability to determine sensitivities and guide policies/decisions/visions. Mathematical modeling and object-oriented frameworks for the design of system-of-systems; approaches for robust design, including robust control analogies and uncertainty modeling/management in multidisciplinary design are just a few if the key elements of the SoSL's teams efforts. In order to achieve this mission statement, the methodology of our undertaking for system-of-systems is summarized on this webpage.
System-of-systems (SoS) refers to a special class of challenges that extend beyond engineering of complex, monolithic assets. SoS problems are categorized by an evolving collection of distributed, heterogeneous, networks of systems, each of which are capable of independent and useful operations, combined to produce emergent (and enterprise) capabilities not obtainable by individual systems alone. Additional features that make design relatively complex: presence of multiple managerial entities and requiring use of inter-disciplinary modeling and analyses efforts.