## 4 Numerical Example  
A numerical problem is performed to present the real-time application of the proposed mathematical model. Research data was mostly collected from the research study of [PERSON] et al. (2019) except the data regarding solar energy, MQL system and scrap. An electricity monitoring system was integrated with a network system of machine tools to record the time scale and production parameters of manufacturing operations. Electricity consumed by the operations was collected from the units given by electric meters, while diesel and gasoline consumption was obtained from previous records. Suppliers were consulted for data regarding outsourcing and rework operations. Table 3 states the cost data related to the production system along with the demands. Major costs include fixed and variable costs including manufacturing, maintenance, holding, scrap, and MQL. Cost related to MQL system was assumed as \(m\%\) of the manufacturing cost for each item \(A\), \(B\), and \(C\), respectively. It is also reported that coolant management costs constitute 7.5% to 17% of the total manufacturing cost, such that \(m\) averaged 7.5 and 17, i.e., 12.25% of manufacturing cost (S  
Data regarding energy consumption from electricity, diesel, solar, and gasoline utilized in manufacturing, reworking and outsourcing operations are presented as in Table 7. Solar energy as a renewable energy is considered to be added in the manufacturing system to reduce the requirement of electricity. In this numerical example, it is assumed as \(h=20\%\) of electricity consumed, which is why the requirement of electricity requirement is reduced to \(1-h\) in each activity i.e., electricity was fulfilling the requirement of 0.38 kWh for manufacturing item \(A\). Due to addition of 0.0636 kWh of solar energy in the manufacturing system, the electricity consumption reduced to 0.2544 kWh. Similarly, the diesel and gasoline usage for item \(A\) are 0.3184 L and 0.0004 m\({}^{3}\), respectively. Outsourcing data is given in Table 8. Outsourcing data covers the capacity restrictions of suppliers, outsourcing cost, defective and scrap proportion. The proposed model's considered defective rate of the items is assumed as 1.2% and the scrap rate as 2% to support realistic representation of the proposed model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cycle** & **Item** & **Maximum** & **Minimum** & **Warehouse** & **Unit Warehouse** \\
**Time** & **Type** & **Capacity (Units)** & **Capacity (Units)** & **Capacity (m\({}^{3}\))** & **Space (m\({}^{3}\))** \\ \hline
1 & \(A\) & 570 & 80 & 480 & 0.2 \\
2 & \(B\) & 290 & 80 & 480 & 0.2 \\  & \(C\) & 345 & 70 & 256 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Capacity limitation of manufacturing system and warehouse. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cycle Time** & **Item Type** & **Rework Cost (\$/Unit)** & **Scrap Cost (\$/Unit)** & **Additional (\$/Unit)** & **Demand** \\ \hline
1 & \(A\) & 0.116 & 0.083 & 3500 & 800 \\
2 & \(B\) & 0.116 & 0.083 & 2000 & 400 \\  & \(C\) & 0.15 & 0.083 & 500 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data of reworking operations with demand.