# Research on Low-Carbon-Emission Scheduling of Workshop under Uncertainty
## 4 Results  
### Related Data on Workshop Scheduling Model  
There are eight machines in a production workshop, and the set \(\{M_{i}|i\in 1,2,\ldots,8\}\) is used to denote the available processing machines set. The data on machine power and consumption are shown in Table 8.  
The fuzzy processing time is obtained using previous data and staff experience, as shown in Table 9, where the value (0,0,0) indicates that the workpiece's current process is not processed on that machine. Meanwhile, each workpiece has an internal delivery period, as shown in Table 10. The delivery period is rounded to account for subsequent calculations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Machine**} & \multirow{2}{*}{**Processing Power (kW)**} & \multirow{2}{*}{**Idle Power (kW)**} & \multirow{2}{*}{**Coolant Usage (L)**} & \multicolumn{2}{c}{**Coolant Circulation**} \\  & & & & **Cycle (10\({}^{4}\) s)** \\ \hline \(M_{1}\) & 11.5 & 2.45 & 600 & 120 \\ \(M_{2}\) & 12.5 & 1.82 & 600 & 120 \\ \(M_{3}\) & 11.5 & 1.50 & 400 & 90 \\ \(M_{4}\) & 12 & 1.58 & 400 & 90 \\ \(M_{5}\) & 10 & 1.41 & 400 & 90 \\ \(M_{6}\) & 6.5 & 0.45 & 350 & 86 \\ \(M_{7}\) & 7.5 & 0.71 & 350 & 86 \\ \(M_{8}\) & 10 & 1.80 & 350 & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Machine power and coolant consumption. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Workpiece**} & \multirow{2}{*}{**Process**} & \multicolumn{4}{c}{**Machine**} \\ \cline{3-6}  & & \(M_{1}\) & \(M_{2}\) & \(M_{3}\) & \(M_{4}\) \\ \hline \multirow{4}{*}{\(J_{1}\)} & 1 & (85, 90, 92) & (88, 95, 98) & (0, 0, 0) & (79, 82, 85) \\  & 2 & (0, 0, 0) & (35, 40, 45) & (32, 35, 38) & (0, 0, 0) \\  & 3 & (0, 0, 0) & (12, 15, 20) & (0, 0, 0) & (14, 16, 17) \\ \hline \multirow{4}{*}{\(J_{2}\)} & 1 & (0, 0, 0) & (0, 0, 0) & (83, 85, 86) & (80, 81, 83) \\  & 2 & (50, 51, 53) & (45, 49, 51) & (0, 0, 0) & (0, 0, 0) \\  & 3 & (29, 31, 33) & (0, 0, 0) & (0, 0, 0) & (32, 35, 36) \\  & 4 & (0, 0, 0) & (0, 0, 0) & (19, 20, 22) & (0, 0, 0) \\  & 5 & (18, 19, 21) & (17, 19, 22) & (0, 0, 0) & (18, 22, 24) \\  & 6 & (0, 0, 0) & (10, 11, 12) & (0, 0, 0) & (8, 9, 11) \\ \hline \multirow{4}{*}{\(J_{3}\)} & 1 & (81, 83, 85) & (0, 0, 0) & (79, 81, 83) & (0, 0, 0) \\  & 2 & (49, 51, 53) & (47, 49, 52) & (51, 53, 55) & (46, 48, 50) \\  & 3 & (0, 0, 0) & (32, 34, 36) & (0, 0, 0) & (33, 35, 36) \\  & 4 & (0, 0, 0) & (0, 0, 0) & (10, 11, 12) & (0, 0, 0) \\ \hline \multirow{4}{*}{\(J_{4}\)} & 1 & (75, 76, 78) & (72, 73, 74) & (0, 0, 0) & (0, 0, 0) \\  & 2 & (0, 0, 0) & (38, 40, 42) & (38, 39, 42) & (36, 38, 39) \\  & 3 & (10, 12, 13) & (0, 0, 0) & (9, 10, 12) & (9, 10, 11) \\ \hline \multirow{4}{*}{\(J_{5}\)} & 1 & (0, 0, 0) & (52, 55, 56) & (0, 0, 0) & (51, 53, 55) \\  & 2 & (0, 0, 0) & (0, 0, 0) & (0, 0, 0) & (0, 0, 0) \\ \hline \multirow{4}{*}{\(J_{6}\)} & 1 & (81, 82, 83) & (0, 0, 0) & (79, 81, 82) & (0, 0, 0) \\  & 2 & (45, 46, 48) & (0, 0, 0) & (0, 0, 0) & (0, 0, 0) \\  & 3 & (10, 11, 12) & (0, 0, 0) & (9, 12, 13) & (8, 10, 12) \\ \hline \multirow{4}{*}{\(J_{7}\)} & 1 & (0, 0, 0) & (71, 72, 73) & (70, 73, 76) & (0, 0, 0) \\  & 2 & (42, 44, 45) & (0, 0, 0) & (0, 0, 0) & (40, 42, 43) \\ \cline{1-1}  & 3 & (0, 0, 0) & (23, 25, 26) & (22, 23, 24) & (0, 0, 0) \\ \cline{1-1}  & 4 & (0, 0, 0)- & (0, 0, 0) & (8, 9, 11) & (0, 0, 0) \\ \hline \multirow{4}{*}{\(J_{8}\)} & 1 & (81, 83, 85) & (79, 81, 83) & (0, 0, 0) & (0, 0, 0) \\ \cline{1-1}  & 2 & (45, 46, 48) & (43, 45, 46) & (0, 0, 0) & (44, 45, 47) \\ \cline{1-1}  & 3 & (21, 22, 24) & (0, 0, 0) & (19, 21, 22) & (0, 0, 0) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Fuzzy processing time on the machine for each product process (seconds).