Information Entropy-Based Complexity Measurement for Systems Engineering and Trade-Off Analysis

Abstract

This research identifies information entropy-based measures of complexity for use in systems engineering during conceptual design. The sources of increased complexity in engineered systems are explored and identified using an avionics example. Measures of architecture complexity are defined for both the resource and functional domains. A suitable measure of system design complexity is identified that is based on axiomatic design principles. Taken together, these measures form the foundation for creating a trade-off analysis capability where changes in architecture and design complexity can be compared against other system attributes and performance. Ultimately this will allow more informed decisions by system engineers and architects to meet performance, timeliness, and affordability objectives when designing complex systems.