## 4 Simulation Test
### Simulation Example Analysis  
Through the simulation analysis of this paper, the following information was obtained: the best scheduling scheme, the start/end processing time of each workpiece in different work centers, the handling time of each workpiece in different work centers, the total number of handling events, the setup times of the processing equipment, etc. Processing and manufacturing companies can combine the above information with their actual situation to arrange a reasonable production plan. The real-time status of processing equipment, vehicles, and workpieces can be monitored throughout the process, thus helping intelligent manufacturing. An example analysis is as follows. Enterprise A receives a multi-variety, small-batch processing order 1 from a customer. \(n=18\) and \(r=3\) (the number of different types of workpieces is: five, three, and ten), and the order is limited to 10 h. After receiving order 1, enterprise A automatically performs calculations in the background and comes up with the following content: order 1 needs to go through three work centers; the current number of available equipment in each work center is three, two, and two; and the handling time is 10.5. The equipment processing time and setup time are shown in **J**(1) and **J**(2); the column is the processing equipment and the row indicates the type of workpiece. \[\textbf{J}(1)= \begin{pmatrix}30&29&28&35&27&44&40\\ 25&26&24&33&14&37&35\\ 10&9&12&36&32&29&33\end{pmatrix} \]  
\[\textbf{J}(2)= \begin{pmatrix}5&4&3&2&4&3&2\\ 5&4&2&3&2&5&3\\ 3&3&2&4&5&4&4\end{pmatrix} \]  
In addition, the equipment processing cost is $72/hour, the handling equipment cost is $30/time, and the current number of available handling equipment pieces is 10. The factory must choose a reasonable scheduling plan according to the order delivery time and its benefits. Using the model calculation method of this paper, the case was simulated and analyzed. Figure 9 shows the Pareto frontier for order 1. Figure 8: Non-dominated solution Gantt chart. From the Pareto frontier obtained in Figure 9, the scheduling scheme for the current optimum was selected. First, considering the constraints of delivery time and the number of handling equipment pieces, it was concluded that there were five options to choose from: (409, 9), (435, 8), (466, 7), (483, 6), and (552, 5). Next, the optimal production solution was selected. The five solutions cost $812.4, $5759.6, $5769.2, $5762, and $5760.8 in order, so (435, 8) was the optimal production solution for order 1. Finally, the processing Gantt chart of the optimal production solution was generated, as shown in Figure 10.  
The number on the rectangular block is the workpiece number, and the equipment had the corresponding setup time when processing different workpieces, so workpiece 4 in M\({}_{1-1}\) was not processed from moment 0. Different types of workpieces were processed on the same equipment with the corresponding equipment setup time, such as workpiece 1 and workpiece 11 in M\({}_{1-1}\). The same types of workpieces were processed on the same equipment without the setup time, such as workpiece 11 and workpiece 12 in M\({}_{2-1}\). As can be seen from Figure 10, all workpieces were processed continuously on the equipment,  
Figure 10: Optimal scheduling solution. Figure 9: Pareto front. and the equipment had no idle time during the processing cycle. The optimal scheduling processing order in Figure 10 was: 16-9-1-11-2-12-17-14-6-13-10-4-5-15-8-7-3-18. Its specific production operation plan was: eT\({}_{B}^{P}\), \(T_{E}^{P}\), \(T_{B}^{S}\), \(T_{E}^{S}\), \(T_{B}^{C}\), \(T_{E}^{C}\), \(N^{T}\). BN was calculated as shown in Figure 11.  
\[\mathbf{T}_{B}^{P}=\]  
\[ \begin{bmatrix}3&3&3&12&18&21&30&33&43&45&51&60&66&72&85&90&96&144\\ 23&23&59&59&86&95&118&131&152&171&167&205&207&239&243&273&278&292\\ 100&100&135&129&161&179&209&212&243&245&278&283&313&331&356&365&393&406\\ \end{bmatrix} \]  
\[\mathbf{T}_{B}^{P}=\]  
\[