Multi-Source Change Path Optimization Method for Complex Products Based on Complex Networks

In the design of complex products, change propagation often leads to overlapping impact paths, structural conflicts, and decreased coordination efficiency, especially in multi-source change scenarios. These issues limit the effectiveness of existing path extraction and management methods. This study addresses the challenge of managing multi-source change propagation by proposing a novel optimization method. The method involves constructing a multi-layer propagation network model that integrates structural, functional, and form-level information and combines propagation risk assessment with frequency feedback mechanisms. By employing a path cost function based on node risks and propagation probabilities, the model accurately evaluates the costs of change propagation and identifies high-risk paths while prioritizing those with lower costs. Additionally, a multi-source path combination optimization approach is developed, and it dynamically adjusts path costs to reduce overlap and conflicts, improving coordination between paths in multi-source change scenarios. The proposed approach was validated through a case study based on the design data of an intelligent cabin system, where the constructed propagation network reflects real-world structural, functional, and form-level dependencies. The experimental results show that the proposed method reduces total propagation cost by an average of 27.3% and lowers node-level conflict rate by 41.6% compared with baseline methods. Tests on different complex products confirmed consistent advantages over baselines with (Formula presented.), while also reducing redundancy and conflicts. The method maintained these advantages across networks of different sizes with linear scalability. Sensitivity analysis confirms that frequency-feedback weighting (Formula presented.) is the main driver for cost reduction, and the overlap penalty (Formula presented.) effectively suppresses node congestion. The method provides a robust solution for managing change propagation in complex product design and enhances overall propagation efficiency and system adaptability.