# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 1 Introduction  
The rapid advancement of information technology, combined with the growing demand for personalised and customised products, has spurred a significant transformation within the manufacturing industry. Many enterprises are shifting from traditional production-oriented models to service-oriented models. In the context of Industry 4.0, the emergence of Cloud Manufacturing provides a good solution for the Service-Oriented Manufacturing model [1]. Cloud Manufacturing(CMfg), proposed in 2010, is a new networked manufacturing model that utilises networks and CMfg service platforms to organise online manufacturing resources based on user demands, providing various on-demand manufacturing services to users [2]. Consumers submit their manufacturing requirements to the CMfg platform, while service providers submit their available manufacturing services. This study focuseson external Cloud Manufacturing, where manufacturers share idle resources through a networked platform. External CMfg is particularly suitable for small- to medium-sized enterprises (SMEs) and highly dynamic production environments, as it offers greater scalability, flexibility, and the ability to efficiently share resources with external partners, especially in scenarios with high levels of standardization and automation. Researchers established various models and proposed different methods to address the optimal allocation of manufacturing services and demands, known as Supply-Demand Matching (SDM). These methods encompass semantic recognition [3; 4; 5], Quality of Service (QoS) [6; 7; 8; 9; 10], multi-agent optimisation [11; 12; 13], complex networks [14; 15; 16; 17], service composition [18; 19; 20], among others. Most existing studies primarily focus on the static SDM issue at particular moments. However, in practical manufacturing scenarios, both manufacturing services and demands exhibit uncontrollable dynamics and uncertainties, such as machine failures, order changes, and random arrivals of supply and demand. Industry 4.0 enables intelligent manufacturing with advanced technologies and digital tools. In recent years, much attention has been paid to Industry 5.0, which is moving towards a sustainable, human-centric, and resilient industry [21]. Resilience in this context refers to the need for greater robustness in industrial production [22]. It is not only reflected in technological flexibility and responsiveness but also in the reliability and continuous operational capability at the system level. Therefore, improving cloud manufacturing's adaptability to demand fluctuations and enhancing the resilience and reliability of manufacturing systems in dynamic environments has become a new challenge for its future development. The hypernetwork model is a powerful tool for modeling complex systems, helping to understand and optimise the dynamic interactions between various services and demands within manufacturing systems. In the face of rapidly changing personalised demands, manufacturing systems require robust adaptability. Therefore, it is crucial to consider the resilience characteristics and sustainable operational capabilities of systems. The supply-demand relationships of manufacturing resources are abstracted into a network model by the hypernetwork modeling approach. This modeling approach could utilise characteristics of complex networks such as connectivity, node correlation, and modular structure to measure network resilience [23], which closely correlates with the resilience of manufacturing systems. Existing research primarily focuses on the satisfaction and QoS utility indicators of cloud manufacturing participants. While some studies have explored the impact of different network structures on supply chain network resilience, there is still a lack of research that simultaneously considers participant satisfaction and network resilience when evaluating the generated matching schemes.