Abstract

This study addresses the significant challenges faced by the nuclear industry in managing complex information systems, where traditional approaches often result in inefficiencies, inconsistencies, and inaccuracies that can have serious consequences for plant performance. By applying graph theory to the analysis of nuclear data flow and integrating the concept of Human System Integration (HSI) through the Technology Organization People (TOP) model, this research proposes an innovative methodology for optimizing data management and improving efficiency in nuclear facilities throughout their lifecycle.

The TOP model provides a comprehensive framework that considers technological, organizational, and social aspects of complex systems, providing a holistic approach to understanding interactions within the information system. Through the application of graph theory methods such as betweenness centrality measure and community detection, this study aims to identify critical nodes and group similar nodes that share common data transmission characteristics, ultimately facilitating more informed data management decisions  and data flow optimization. The results demonstrate the potential of this approach to address the complex information management challenges faced by the nuclear industry, with implications for improving the performance and efficiency of nuclear facilities.

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