Correlation analysis of failure risk factors in automated container port logistics systems from a resilience perspective

This paper aims to explore the correlations among failure risk factors of automated container port logistics systems from a resilience perspective. Based on the Wuli-Shili-Renli (WSR) theory and a multi-dimensional resilience framework, five key categories of risk factors affecting system resilience are identified: port infrastructure, port logistics operation organization, port personnel, port operational environment, and port emergency response and management. Through expert interviews, system component analysis, and a review of related literature across these five dimensions, 36 specific risk factors were identified to establish an initial set of system failure risk factors. Subsequently, a dual-factor screening method combining grey relational analysis and rank correlation was applied to simplify the initial set, constructing a system failure risk factor index system from a resilience perspective. Finally, a comprehensive model combining the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method and the Total Adversarial Interpretation Structure Model (TAISM) was proposed. This model identifies and quantifies the complex correlations among system failure risk factors from a resilience perspective, revealing their relative importance and their impact on system resilience. The results indicate that system resilience is primarily influenced by internal factors such as the smoothness of collection and distribution operations, infrastructure service capacity, and the stability of technical support systems. External factors, such as the prevention capability for major emergency threats and the improvement of government supervision and coordination feedback mechanisms, also significantly impact system resilience. The complex interactions among key factors have a profound effect on system resilience, underscoring the need to enhance coordination and management measures to strengthen overall system resilience. This study provides a theoretical foundation for developing targeted strategies to enhance the resilience of automated container port logistics systems. • Novel FDEMATEL-TAISM Model: This paper proposes a novel model combining Fuzzy DEMATEL and Total Adversarial Interpretation Structure Model (TAISM) to analyze the complex correlations between failure risk factors in automated container port logistics systems from a resilience perspective • System Failure Risk Factor Screening: By integrating grey relational analysis and rank correlation in a dual-factor screening method, the study simplifies and constructs a system failure risk factor index system from a resilience perspective. • Identification of Key Risk Factors: The study identifies five major risk types—port infrastructure, port logistics operation organization, port environment, port emergency response and management, and Port employees—using WSR theory, with a focus on the resilience impact of each on the system. • Resilience-Centric Risk Analysis: The results show that system resilience is mainly influenced by internal factors like the smoothness of operations, infrastructure capacity, and technical support stability, along with external factors such as emergency prevention capabilities and improved government coordination. The complex interactions among these factors highlight the need for better coordination and management to enhance overall system resilience. • Case Study and Practical Implications: Using Qingdao Port as a case study, the research provides practical insights for enhancing resilience in automated port logistics systems, emphasizing the need for improved coordination, infrastructure maintenance, and emergency management.