## 4 Results  
In this research, the length of a working day's shift was divided by half an hour (considering the shortest possible duration of the order). For this purpose, a matrix for orders is created. Data in it are divided by half an hour. Each 30 min can represent a different operation and the order of operations can be changed every 30 min. As an example, one shift working plan is presented in Figure 5. Colors mark different operations from Table 5. This shift works on several orders but from the chart, it can be seen that many orders are not in a process. Additionally, the matrix of the performed tasks for each working employee is created. It represents the operation being performed by each employee and which employee has no task--white spaces in Figure 6.  
In this paper, real-life cases are researched, and production plans consist of 29 previously mentioned production orders. In total, 18 employees were working in 1 shift, and 13 operations can be performed. The first check was when optimization was not performed. If production orders follow input order date, after 16 working hours, the working plan will be as presented in Figure 7a, and the operations of the employees will be as in Figure 7b. \begin{table}
\begin{tabular}{c c} \hline \hline
**Employee No.** & **Hourly Wage (Euros)** \\ \hline
10 & 4.75 \\ \hline
11 & 4.75 \\ \hline
12 & 4.75 \\ \hline
13 & 7 \\ \hline
14 & 7 \\ \hline
15 & 6 \\ \hline
16 & 4.75 \\ \hline
17 & 4.75 \\ \hline
18 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Operation No.** & **Hourly Price (Euros)** \\ \hline
1 & 5 \\ \hline
4 & 3 \\ \hline
5 & 1 \\ \hline
6 & 5 \\ \hline
7 & 10 \\ \hline
8 & 2 \\ \hline
9 & 1 \\ \hline
10 & 7 \\ \hline
11 & 20 \\ \hline
12 & 15 \\ \hline
13 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Machinery hourly price. As seen in Figure 7a, only a few orders are performed after 16 h; thus, the machinery and employees are not sufficiently loaded, as shown in Figure 7b. Based on that, optimization should be performed to improve these results. The present study involves the computation of the duration for each order, in accordance with the associated operations. Subsequently, the average duration of the orders was determined, and the orders with a duration exceeding the average were fragmented into smaller units. This fragmentation strategy ensures that the order duration does not surpass the computed average, and that the allocation of operations to employees is proportionate. The net outcome of this strategy was a rise in the number of orders to 44, as large orders were disintegrated into smaller units. The findings presented in Figure 8 demonstrate that although there was an improvement in the number of running orders after 16 h, the figures remained suboptimal (Figure 8a). The optimization measures resulted in increased task assignments for the employees (Figure 8b). Figure 5: One shift working plan. Figure 6: Operations are divided by employees. A further round of optimization was performed, whereby the production orders were prioritized based on several critical factors. Notably, the order quantity, payment type, customer rating, and order duration were identified as having the most significant impact on overall profitability. Therefore, these factors were used to rank and schedule the production orders for enhanced profitability. Based on the latest findings, after this optimization, it was evident that the number of active production orders remained relatively constant (Figure 9a). However, there was a notable increase in the diversity of tasks performed by employees, resulting in a higher workload for the machinery (Figure 9b). Despite this, it is noteworthy that employees are not operating at their maximum capacity, suggesting that the current workforce may not be optimally aligned with the available machinery resources. Following an analysis that identified some employees who were deemed surplus to requirements, a third round of optimization was undertaken.