# Research on Low-Carbon-Emission Scheduling of Workshop under Uncertainty
## 4 Results
### Solving of Model Based on NSGA-III-ST  
The model is solved using the NSGA-III and NSGA-III-ST algorithms. We set the initial population size as 100, the variation probability as 0.8, and the number of iterations as 100. The simulation software used in this paper is Pycharm 2022.1.2, and the simulation experiment environment is AMD Ryzen 5 5600H with Radeon Graphics, 3.30 GHz. The RAM is 16G, with the 64-bit Windows 11-based operating system for x64 processors. The two algorithms run 10 times independently, and the results are illustrated in Table 12.  
Table 12 shows that the best value (Best) obtained by the NSGA-III-ST algorithm is superior to those obtained by the NSGA-III algorithm, and the average of the \(SCE\) index obtained by NSGA-III-ST is better than those of NSGA-III. Meanwhile, we run the NSGA-III-ST against the NSGA-III using the test functions DTLZ1-4, finding that the difference in running time between the two algorithms for the test functions is insignificant.  
Figure 7 plots the Pareto solution sets obtained by the two algorithms. The pentagram represents the solution set obtained by the NSGA-III-ST, and the diamond denotes that of NSGA-III. Most of the diamond points are dominated by the pentagram points, which means that the ability of NSGA-III-ST to obtain Pareto solutions is outstanding. Considering that the NSGA-III-ST algorithm outperforms the NSGA-III in terms of solution performance, the NSGA-III-ST algorithm has a higher efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & **Number** & **Rated Power (kW)** & **Total Power (kW)** \\ \hline Management Kanban & 8 & 0.1 & 0.8 \\ Energy-saving fluorescent & 20 & 0.03 & 0.6 \\ lamp of T8 type & & & \\ Axial flow fans & 5 & 1.1 & 5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Number and rated power of each public facility.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Type**} & **Parameter** & \multicolumn{4}{c}{**NSGA-III**} & \multicolumn{4}{c}{**NSGA-III-ST**} \\ \cline{3-8} \multicolumn{1}{c}{} & & _TBI_ & _SCE_ & \(\mathbf{\Delta\delta}\) & _TBI_ & _SCE_ & \(\mathbf{\Delta\delta}\) \\ \hline \multirow{3}{*}{Value} & Best & 0.08 & 136.13 & 197.32 & 0.03 & 133.51 & 195.71 \\  & Average & 0.90 & 142.71 & 216.48 & 1.78 & 135.81 & 206.32 \\ \hline \multirow{3}{*}{Optimal} & _TBI_ minimization & 0.08 & 145.43 & 236.35 & 0.03 & 143.39 & 231.31 \\  & _SCE_ minimization & 2.52 & 136.13 & 219.83 & 0.30 & 133.51 & 228.76 \\  & \(\Delta\delta\) minimization & 2.36 & 143.08 & 197.32 & 1.99 & 136.69 & 195.71 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Example results of solving for the two algorithms.  
Figure 7: Pareto solution set obtained by the two algorithms.  
Therefore, the following improvements can be made: for equipment with relatively concentrated operations, replacing it with lower-energy-consuming equipment or considering adjusting processes; while meeting internal delivery requirements, the external delivery period should also be ensured, that is, meeting the customer's needs in terms of both time and quality to improve customer satisfaction.