Dynamic Resilience Assessment of Aviation Logistics Supply Chains

Abstract

The complexity and dynamism of the aviation logistics supply chain make it difficult to accurately evaluate its resilience using traditional analysis methods, especially in capturing network topology, nonlinear interactions, dynamic evolution, and disturbance transmission mechanisms. Based on complex network theory, this study establishes three objective layers, including “capability, management, adaptation/evolution,” which are further refined into seven criterion layers comprising 19 quantifiable indicators. Unascertained measurement functions characterize the correlation between indicators and resilience levels, while a Dynamic Resilience Intelligent Assessment Model for Aviation Logistics Supply Chain (ALSC-DR-IAM) integrating gradient iteration and simulated annealing algorithms is proposed. Experimental results across different datasets show the model achieves a disturbance recovery rate of 97.86%, key node redundancy processing efficiency of 0.8923, path optimization time of 15.678 ms, and data adaptability index of 0.9421 on different datasets. The model outperforms comparison models in evaluation efficiency, bandwidth utilization, prediction accuracy, and system response stability, while demonstrating superior performance in core indicators, including key node redundancy, disturbance recovery rate, and path optimization. The research provides a technical framework combining theoretical innovation and practical value for quantitatively evaluating and dynamically optimizing the aviation logistics supply chain, thereby enhancing robust supply chain management under complex disturbances.