## 6 Conclusions and Future Research  
In this paper, a modified steady-state genetic algorithm was tested to solve the optimization problem. The problem of determining the batch order and the size of three \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{C_{\text{fitness}}}\) & \(\mathbf{B_{\text{D}}}\) & \(\mathbf{B_{\text{E}}}\) & \(\mathbf{B_{\text{F}}}\) & \(\mathbf{F_{1\_MAX}}\) & \(\mathbf{F_{2\_MAX}}\) & \(\mathbf{F_{3\_MAX}}\) & \(\mathbf{F_{4\_MAX}}\) \\ \hline
155.610 & 119 & 295 & 164 & 595 & 238 & 164 & 357 \\
156.534 & 123 & 225 & 159 & 680 & 434 & 159 & 492 \\
156.827 & 161 & 156 & 147 & 591 & 322 & 156 & 322 \\
157.173 & 230 & 267 & 170 & 526 & 267 & 198 & 340 \\
157.273 & 229 & 182 & 253 & 676 & 265 & 182 & 482 \\
157.309 & 224 & 200 & 300 & 704 & 360 & 216 & 448 \\
157.314 & 201 & 233 & 255 & 605 & 267 & 66 & 468 \\
157.334 & 137 & 135 & 209 & 643 & 369 & 137 & 542 \\
157.455 & 184 & 260 & 175 & 568 & 260 & 184 & 368 \\
157.603 & 178 & 126 & 157 & 544 & 178 & 157 & 304 \\ \multicolumn{7}{c}{_Average:_} & 613.2 & 296.0 & 161.9 & 412.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Required buffer configuration. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Buffer Configuration** & \(\mathbf{F_{1}}\) & \(\mathbf{F_{2}}\) & \(\mathbf{F_{3}}\) & \(\mathbf{F_{4}}\) & \(\mathbf{B_{\text{D}}}\) & \(\mathbf{B_{\text{E}}}\) & \(\mathbf{B_{\text{F}}}\) & **Average \(\mathbf{C_{\text{fitness}}}\)** & **Best \(\mathbf{C_{\text{fitness}}}\)** \\ \hline BC1 & 90 & 90 & 90 & 90 & 87 & 86 & 75 & 163.787 & 159.038 \\ BC2 & 90 & 60 & 90 & 120 & 60 & 60 & 59 & 171.562 & 166.604 \\ BC3 & 90 & 90 & 60 & 120 & 54 & 60 & 60 & 171.353 & 165.592 \\ BC4 & 120 & 60 & 90 & 90 & 56 & 60 & 57 & 171.526 & 165.842 \\ BC5 & 120 & 90 & 60 & 90 & 60 & 60 & 59 & 171.562 & 166.604 \\ BC6 & 120 & 60 & 60 & 120 & 60 & 60 & 53 & 170.464 & 165.383 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Buffer configurations and results. technologically similar products in a hybrid flow shop with a limited buffer capacity was taken from the real environment. The results that were obtained in chapter 5 of this research show that both the performance and speed of this algorithm are quite good, and that the development of highly specialized and custom-made software can be taken into consideration during the process of production planning. In short, the low execution time of the algorithm allows for the testing of many possible scenarios, and consequently, it obtains a better production process configuration. The results also show that the software based on the developed algorithm can serve as a reliable tool for its use in production planning. This can be very important in cases where there is not much available time to make new production configurations from scratch or to reconfigure existing ones. Although genetic algorithms have been recognized as effective search algorithms for many years, continuous improvements in computer performances open new possibilities for the further development of genetic algorithms. In future research, the performance of the genetic algorithm that is used in this paper will be tested on even more complex problems to prove the capability of the algorithm to obtain reliable results. The GA that was developed and used in this research is very suitable for parallelization and its use on modern multicore CPUs. It can be assumed that the parallelization of the computer code for the proposed genetic algorithm could bring significant performance improvements, and that the algorithm can work on highly parallelized computing systems. In this paper, we used a single thread solution, and so, a further reduction of the computational time is possible with some code modifications being performed. In further research, it is possible to investigate single multi-tread vs. multiple single thread solutions. Conceptualization, G.J. and D.I. ; methodology, D.I. ; software, G.J. ; validation, G.J., D.I. and Z.J. ; formal analysis and data curation, M.P. ; resources, Z.J. and M.P.