Measuring Complexity of Engineering Systems: A System State and Behavioral Approach
Open AccessSystem complexity has grown exponentially over the last three decades, but the development of complexity measures to quantify system complexity is still in its infancy. This research proposes a comprehensive measure and methodology to quantify the complexity of engineering systems. The complexity measure and associated methodology are based on a holistic system approach and formulate an engineering system’s complexity as a single quantifiable system property that simultaneously incorporates system structure, function, states, and behavior. Most current measures only quantify either structural complexity or functional complexity. However, the proposed comprehensive complexity measure includes both structure and function, and also states and behavior, enabling the objective assessment of different engineering designs, identification of elements driving significant complexity, and management of system complexity.Complex engineering systems are characterized by large structures with complicated configurations consisting of numerous components and interconnections. The interrelations among components become very important, and new functionality and system states may develop, challenging accurate predictions of system behavior. Structure, function, states, and behavior are critical characteristics to consider in assessing modern systems’ complexity. These system characteristics are intertwined and have a profound impact on system design and future system performance. The proposed complexity measure is derived from a range of disciplines and principles, including quantum mechanics, signal processing, and systems engineering. This dissertation contains two fundamental parts. In the first, the proposed complexity measure is formulated, following a mathematical modeling methodology. In the second, a system model is developed, and the proposed complexity measure is applied to assess the system model’s complexity. An ATM system model was created using a Model-Based Systems Engineering (MBSE) modeling tool. A simulation study was performed to obtain the output data consisting of the values of the complexity quantity associated with the system. Systems engineering professionals can apply the proposed measure to quantify system complexity and, therefore, identify complex elements and compare different system designs or different systems. The proposed complexity measure, integrated with the modeling methodology, enables these professionals to get a more comprehensive understanding of system complexity. The complexity measure provides a solid basis for evaluating and communicating complexity during system design and architectural modeling and improving scheduling and cost estimates during system development.
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