Enhancing the management of nuclear information systems through graph theory-based methods and human-centered modeling

The integration of digital technologies is crucial for enhancing the efficiency and performance of nuclear facilities throughout their entire lifecycle. However, the nuclear industry faces significant challenges due to its intricate supply chain, resulting in fragmented data exchanges and inefficiencies. The expansion of complex information systems has introduced considerable challenges to data management, leading to inconsistencies and inaccuracies that adversely affect operational performance.To address these challenges, this study proposes an integrated analytical approach that combines graph theory with the Technology-Organization-People (TOP) Model for Human-System Integration (HSI). By incorporating the TOP model, we consider the technological, organizational, and human dimensions of complex sociotechnical systems. In addition, a method is introduced to weight edges in data flow graphs, applied to measure the effect of cognitive load of human tasks.A synthetically generated dataset was used to simulate real-world operations, allowing the application of two graph theory methods: Betweenness Centrality to identify critical nodes and Spectral Clustering to group nodes with similar dataflow characteristics, providing insight into the underlying structure and dynamics of the nuclear dataflow. This approach facilitates a more sophisticated analysis and comparison of results, distinguishing between outcomes with and without cognitive load and supports then more informed data management and flow optimization decisions.The results of this study underscore the effectiveness of combining graph theory methods with human-centered models and also highlight the critical role of human factors in data management strategies that subsequently contribute to improved efficiency, reliability, and performance of nuclear facilities throughout their life cycle.