# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 4 Dynamic Matching Method
### Specific Mechanism of Rematching
A comparative analysis with traditional methods that solely focus on operational utility demonstrates that the proposed method achieves superior performance in several key areas: faster rematching time, higher operational utility, improved network connectivity, and better load balance under dynamic conditions. These results highlight the practical value of integrating network resilience into SDM models, particularly in environments characterised by frequent disruptions and uncertainties. Through sensitivity analysis, we also get several key parameters that have a great influence on the model. For industry practitioners, this study provides a practical framework for designing resilient cloud manufacturing platforms that can effectively respond to personalised and dynamic production demands. By integrating network resilience into SDM processes, manufacturers can achieve higher operational efficiency, reduce downtime, and improve customer satisfaction. The proposed approach is particularly valuable for industries facing volatile market conditions and complex supply chains, such as automotive, aerospace, and electronics manufacturing. For scholars, this study offers a new perspective on integrating network resilience into supply-demand matching models, opening up several avenues for future research. The hypernetwork-based modeling approach can be extended to other domains, such as logistics, healthcare, and energy systems, where resilience and adaptability are critical. Additionally, the integration of advanced technologies, such as digital twins, the Internet of Things (IoT), and artificial intelligence, presents exciting opportunities for further enhancing the capabilities of intelligent manufacturing systems. Figure 9: Results of sobol analysis of key parameters. However, this study has some limitations. The hypernetwork model employed in this paper is an idealised representation of real-world manufacturing systems, and challenges may arise when collecting and integrating real-world data due to its complexity and heterogeneity. Currently, the experiments have only validated the method's effectiveness through data simulation, and has not been verified with real industrial data, which is one of the limitations of this paper. Additionally, the current approach primarily focuses on adjusting and recovering disrupted solutions after a disruption has occurred. While this reactive strategy is effective, it may not fully leverage the potential of proactive measures. Future research could explore adaptive methods that preemptively address dynamic SDM, such as predictive analytics and machine learning techniques, to anticipate disruptions and initiate adjustments before they occur. Furthermore, integrating real-time data streams and advanced optimisation algorithms could further enhance the platform's responsiveness and robustness. In conclusion, this study contributes to the field of cloud manufacturing by providing a comprehensive framework for resilient supply-demand matching. The proposed approach not only improves operational efficiency but also strengthens the system's ability to withstand and recover from disruptions. By addressing the identified limitations and exploring new directions, future research can build upon this work to develop even more advanced and practical solutions for intelligent and resilient manufacturing systems. Formal analysis, J.Z. ; Supervision, C.W. ; Validation, C.W. ; Visualization, J.Z. ; Writing--original draft, J.Z. ; Writing--review and editing, C.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. ## References

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