## 6 Case Study
### Discussion  
As observed from the iterative curves depicted in Figure 12, the penalty value of the optimal individual in the final population demonstrates significant optimization when compared to the average penalty value of the initial population, which is produced randomly. Notably, there is a substantial 6.5 times reduction in penalty value for the overall grouping method. Furthermore, when optimizing the 100 mm, 200 mm, and 270 mm groups individually, the penalties decrease by factors of 4.6, 6.4, and 3.3, respectively. These results highlight the considerable improvements achieved through the optimization process. Moreover, the results further demonstrate that the optimization process remains uncompromised even with the increase in computational cost resulting from a larger number of building components. In fact, it is observed that the overall optimization method, which involves the highest number of components, exhibits the most significant decrease in penalty value. This finding attests to the robustness of the intelligent algorithm employed in this study, affirming its capability to handle complex scenarios and deliver effective optimization results.  
When comparing the performance of the overall optimization and grouping optimization, the following three points can be found: (i.) The grouping or overall optimization methods offer basically the same treatment for the local connection relations, as shown in the two examples in Figure 15; (ii.) The grouping optimization achieves the optimization of the interference penalty value in the grouping state, which may not be globally optimal. But the overall optimization strictly adhered to the optimization criteria of minimizing layout interference as much as possible, achieving the global optimum of the interference penalty value; (iii.) By comparing the total penalties, it can be observed that the overall optimization method outperforms the grouping optimization method, although the overall optimization requires more computational resources.  
Figure 14: Final assembly sequence of entire components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subgroup** & \(\mathbf{P}_{weight}\) & \(\mathbf{P}_{space}\) & \(\mathbf{P}_{layout}\) & \(\mathbf{P}_{total}\) \\ \hline
270 mm & 6.68 & 12.27 & 0 & 18.95 \\ \hline
200 mm & 26.17 & 11.07 & 0 & 37.24 \\ \hline
100 mm & 0 & 0 & 6.5 & 32.5 \\ \hline Grouping & 170.63 & 50.09 & 6.5 & 383.22 \\ \hline Overall & 135.81 & 135.23 & 2.1 & 323.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Penalty of three subgroups.  
In general, we recommend prioritizing the overall optimization method because the layout interference between components significantly impacts the assembly difficulty and should be avoided. And this viewpoint guides the entire optimization process. Indeed, the results of grouping optimization are also acceptable when there are situations where overall optimization cannot be realized.  
Once the construction schedule is settled, it can be imported into Microsoft Project 2016 to generate a Gantt chart. Navisworks 2020 offers a platform to integrate the construction schedule with the BIM model to create an animated construction process. The schedule information is also utilized to update the information in the graph database, thereby elevating the stored 3D BIM to a 4D model, resulting in a bidirectional flow of information in the graph database. Figure 16 provides visual evidence of this process.