Supply–Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model

In response to the escalating demand for personalisation and customisation, the manufacturing industry is increasingly driven towards digital and intelligent transformation. The fluctuating market environment and variable consumer demands impose significant challenges on the resilience of manufacturing systems. With the advent of Industry 5.0, developing a resilient manufacturing system has become even more critical. While existing studies have extensively explored participant satisfaction in cloud manufacturing, there remains a notable gap in understanding how network resilience impacts supply–demand matching, particularly in dynamic and personalised production scenarios. To address this gap, this study employs complex system modeling, integrating complex network characteristics for matching modeling and optimising dynamic adjustment strategies. By incorporating network resilience into the matching process, the proposed approach enhances both the robustness and operational efficiency of manufacturing systems. Experimental results demonstrate that the proposed approach significantly improves system adaptability and reduces the impact of disruptions, offering a practical solution for industry practitioners to design resilient cloud manufacturing platforms. For scholars, this study provides a new perspective on integrating network resilience into supply–demand matching models, creating new opportunities for future research in intelligent and resilient manufacturing systems.