# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 4 Dynamic Matching Method
### Specific Mechanism of Rematching
Since the model is a multi-objective problem, the overall Pareto solution set for each sample output was processed. The median solution from each set of Pareto solutions was weighted, transforming it into a composite value to serve as input for Sobol calculations. Figure 9 presents the analysis results obtained using SALib in Python. In Figure 9, the first-order effect represents the independent contribution of a single input parameter to the output, while the total-order effect accounts for both the individual contribution of the parameter and its interactions with other parameters. From the data in the figure, it can be observed that the first-order effect of the \(C_{T}\) parameter is 0.389, the largest among the three key parameters, indicating that its individual impact on the model's sensitivity is significant. For the \(B_{T}\) parameter, the first-order effect is 0.261, while the total-order effect is 0.392. This indicates that the interaction between this parameter and others has a considerable influence on the model's output, increasing by 0.131. The global sensitivity analysis further confirms that the \(C_{T}\) parameter has the greatest sensitivity impact on the model. Therefore, in real manufacturing system operations, the maximum cost \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Disturbance** & **U1** & **U2** & **S1\({}^{\prime}\)** & **S2\({}^{\prime}\)** & **S1** & **S2** \\ \hline \multirow{3}{*}{\(C_{S}\)} & \(-10\%\) & 464.419 & 449.828 & \(-0.200\) & \(-0.421\) & \(-0.361\) & \(\mathbf{-0.725}\)* \\  & \(+10\%\) & 447.963 & 430.886 & \(-0.162\) & \(-0.304\) & & \\  & \(-10\%\) & 450.866 & 446.138 & 0.098 & \(-0.28\) & & \\  & \(+10\%\) & 444.044 & 433.359 & \(-0.248\) & \(-0.209\) & \(-0.15\) & \(-0.489\) \\  & \(-10\%\) & 460.665 & 429.892 & 0.117 & 0.104 & & \\  & \(+10\%\) & 465.264 & 445.521 & 0.218 & 0.186 & & \\  & \(-10\%\) & 472.212 & 436.106 & 0.371 & 0.342 & & \\  & \(+10\%\) & 458.236 & 443.68 & 0.064 & 0.256 & & \\  & \(-10\%\) & 461.72 & 434.913 & 0.141 & 0.15 & & \\  & \(+10\%\) & 465.719 & 451.583 & 0.228 & 0.488 & & \\  & \(-10\%\) & 445.903 & 443.405 & \(-0.207\) & \(-0.175\) & & \\  & \(+10\%\) & 447.225 & 432.764 & \(-0.178\) & \(-0.232\) & \(-0.385\) & \(-0.407\) \\  & \(-10\%\) & 467.897 & 435.894 & 0.276 & 0.112 & & \\  & \(+10\%\) & 458.996 & 443.1166 & 0.081 & 0.164 & & \\  & \(-10\%\) & 486.721 & 429.851 & 0.690 & 0.344 & & \\  & \(+10\%\) & 496.484 & 457.040 & 0.904 & 0.697 & & \\  & \(-10\%\) & 419.894 & 453.615 & \(-0.778\) & \(-0.566\) & & \\  & \(+10\%\) & 439.064 & 423.168 & \(-0.357\) & \(-0.600\) & & \\  & \(-10\%\) & 457.662 & 434.704 & 0.051 & 0.198 & & \\  & \(+10\%\) & 467.863 & 446.854 & 0.275 & 0.307 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Single parameter local sensitivity analysis results. limit that each demand side can provide is a key factor in determining the overall system performance, making it crucial to obtain more accurate data during the collection process. ## 6 Conclusions

In response to the dynamic and uncertain supply-demand environment in cloud manufacturing (CMfg) platforms, this paper builds upon existing research by abstracting real manufacturing resources using a hypernetwork model to construct a multi-layered network model. The proposed approach introduces a novel perspective for evaluating supply-demand matching (SDM) by simultaneously considering both the platform's operational utility and the network's resilience. This dual focus enables the platform to rapidly respond to customised production demands, accommodating varying scales and complexities of production tasks, thereby significantly enhancing the resilience and adaptability of manufacturing systems. Through multiple sets of experiments, the effectiveness of the proposed approach in addressing SDM issues has been validated.