Multidimensional robustness analysis for optimizing complex systems

This work proposes the development of a metric for the analysis of operational robustness in systems, focusing on performance, complexity, and stability as key components. The methodology integrates these factors, enabling the assessment of the system’s ability to meet its design requirements, its internal dynamics and external interactions, and its capacity to return to equilibrium after disturbances. The metric is applied in three case studies: an intensive care unit, process scheduling in operating systems, and traction and braking in electric vehicles. The results show that, in scenarios with higher robustness, the contributions of performance, complexity and stability are balanced, with performance contributing around 30% and complexity and stability each contributing approximately 35%. In contrast, scenarios with lower robustness exhibit greater variation in the contributions of these components. These findings suggest that the proposed metric is an efficient tool for both quantitative and qualitative analyses, providing more detailed perspectives for decision making in complex systems.