Risk Identification and Network Optimization of Regional Supply Chains Based on Multi-Source Data Fusion
Keywords:
Regionalized supply chain, Multi-source risk data fusion, NSGA-III, Graph convolutional network, Supply chain resilience, Layout optimizationAbstract
In response to increasingly complex global economic dynamics, enterprises are encountering heightened supply chain challenges, including transportation disruptions, frequent shifts in international trade regulations, and the demand for greater supply chain autonomy. This study proposes a network optimization framework based on multi-source risk data fusion and the multi-objective evolutionary algorithm NSGA-III. Four core indicators—logistics efficiency, regulatory adaptability, historical disruption frequency, and substitution costs—are integrated, and node risk features are extracted through graph convolutional networks (GCNs) to provide quantitative assessments of supply chain elements. A simulation was conducted within a multinational component group with an annual procurement volume exceeding USD 800 million, systematically evaluating centralized, zoned, and regionalized collaborative supply chain layouts. The results demonstrate that the "3 Centers + 6 Radiations" regionalized collaborative model achieves a 34.5% reduction in average delivery cycle fluctuations and a 26% decrease in annual plan adjustments, compared to traditional structures. Furthermore, the dynamic elasticity adjustment mechanism developed in this study enables scenario-based simulations under varying geopolitical and economic disruptions, providing robust decision support for resilient supply chain optimization.
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