Leonard A. Wojcik

Systems of systems engineering in the enterprise context: a unifying framework for dynamics

Systems of systems engineering (SoSE) takes place in the broader context of an enterprise, which we define very generally as a purposeful or industrious undertaking. Enterprises of greatest interest for SoSE are typically complex, multi-agent organizations or sets of organizations exhibiting the characteristics of complex adaptive systems, including evolutionary and emergent behaviors at multiple scales. The most fundamental of enterprise purposes are manifest in enterprise operations, in which the enterprise interacts with the larger world external to itself, and this aspect of enterprise dynamics is modeled. SoSE is but one aspect of an enterprises activities, and the whole set of enterprise activities is predominantly oriented towards accomplishing and supporting the enterprises mission in operations. This paper proposes a unifying framework for understanding and modeling the organizational, technical, and system complexities of enterprise dynamics across a range of enterprise types as major acquisition program initiatives are undertaken to provide improved operational capabilities

Emergent Enterprise Dynamics in Optimal Control Models of the System of Systems Engineering Process

An enter-prise engaged in a dynamic mission involving multiple internal and external stakeholders operates as a complex adaptive system. System of systems engineering in such a context, spanning the entire cycle from research and development, through acquisition, transition to operations, operational use and maintenance, and system retirement, is extraordinarily complex. Here, we apply high-level and simplified optimal control-theoretic models of complex system of systems engineering processes to the acquisition of new capabilities or enterprise transformation. The results suggest guidelines and research directions for planning and managing the system of systems engineering processes of an enterprise. The results also provide quantitative benchmarks against which all kinds of enterprise programs, encompassing acquisition of capabilities and operational use towards a mission, can be compared. Such modeling also could be used to support decision-making on selection of acquisition and management approaches for these extremely complex efforts.

Traffic flow management modeling and operational complexity

Traffic flow management (TFM) actions are commonly used to mitigate capacity/demand imbalances within the National Airspace System (NAS). Modeling TFM events has proven challenging in the past, partly because of weather forecast uncertainty, and partly because of the complexity and unpredictability associated with highly-interrelated traffic patterns and distributed decision-making in the NAS. In this paper, we present results of a simulation of a NAS TFM event in which weather effects are relatively small. This facilitates interpretation of the similarities and differences between simulation results and the actual event in terms of NAS operations and decision making, with relatively small weather-related complications. We conclude that TFM modeling shows promise as a tool to aid post-event TFM analysis, but the complex operational factors impose limits on the predictability of outcomes in TFM events. A CAASD-developed fast-time network simulation of the NAS was used for this analysis.

DP AT FLIGHT DELAY MODELING AND ITINERARY TRACKING

MITREs Center for Advanced Aviation System Development (CAASD) has developed a high-speed parallel airframe and airspace simulation tool known as the Detailed Policy Assessment Tool (DPAT). The tool is used for delay analysis of aircraft itineraries as a function of airspace capacities, traffic demand and airline scheduling practices, specifically including schedule padding and slack. Schedule padding is extra time included in a flights scheduled gate-togate time, to allow for possible delays. Schedule slack is the extra time allowed between flights of the same airframe, to allow for possible delays. We will present a simple analytic model of flight delays that show the interrelation of these effects. Such an analysis is possible with a new feature of DP AT that allows analysts to trace itineraries from airport to airport, including traversal of every enroute sector and every restriction encountered. The DP AT itinerary trace is implemented in an XML framework to simplify program debugging, animation, and the external interface to other ATC modeling tools. The effect of airport/sector capacity increase or reduction, traffic demand changes, and schedule padding and slack on flight delays can be tracked explicitly in model validation and verification efforts.

Can Models Capture the Complexity of the Systems Engineering Process

Many large-scale, complex systems engineering (SE) programs have been problematic; a few examples are listed below (Bar-Yam, 2003 and Cullen, 2004), and many others have been late, well over budget, or have failed: Hilton/Marriott/American Airlines system for hotel reservations and flights; 1988–1992;

Analyzing System-Level Performance Trade-Offs Among Future Air Traffic Management Concepts

125 million; “scrapped” Federal Aviation Administration Advanced Automation System; 1982–1994;

Communications, navigation, and surveillance events simulation for the national airspace system

3+billion; “scrapped” Internal Revenue Service tax system modernization; 1989–1997;

Decision support for advanced aviation concepts

4 billion; “scrapped” Boston “Big Dig” highway infrastructure project; roughly

Relationship between airport congestion and at-gate delay

15 billion; about

Flight connections and their impacts on delay propagation

9 to