## 5 Results
### Layout Instance and Optimization  
1. Generating parent layout instance  
The GA approach was applied to generate the parent layout instance. Four trains from the objects database were selected at random, one by one, as the Parent I. Those four trains were brought into the list. Another four trains were selected from the objects database at random again as Parent II. These four trains were also brought into the list (Table 8). The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**RICj** & **X,Y** & **R2 Cj** & **X,Y** & **R3 Cj** & **X,Y** & **R4 Cj** & **X,Y** & **R5 Cj** & **X,Y** & **R6 Cj** & **X,Y** & **R7 Cj** & **X,Y** \\ \hline
**A** & 0,0 & & & & & & & & & & & D & 0,38 \\ R\({}_{1}\)C\({}_{0}\) & 14.0 & R\({}_{2}\)C\({}_{1}\) & 64.3 & R\({}_{3}\)C\({}_{1}\) & 69.15 & R\({}_{4}\)C\({}_{1}\) & 12,17.7 & R\({}_{5}\)C\({}_{1}\) & 12,20.7 & R\({}_{6}\)C\({}_{1}\) & 59.26 & R\({}_{7}\)C\({}_{1}\) & 14.38.4 \\ R\({}_{1}\)C\({}_{2}\) & 22.0 & R\({}_{2}\)C\({}_{2}\) & 84.3 & R\({}_{3}\)C\({}_{2}\) & 72.15 & R\({}_{4}\)C\({}_{2}\) & 32,17.7 & R\({}_{5}\)C\({}_{2}\) & 32,20.7 & R\({}_{6}\)C\({}_{2}\) & 62,26 & R\({}_{7}\)C\({}_{2}\) & 22.38.4 \\ R\({}_{1}\)C\({}_{3}\) & 64.0 & & & & & & & & & & C & & \\
**B** & 84.0 & & & & & & & & & & & R\({}_{7}\)C\({}_{3}\) & 84.38.4 \\ R\({}_{1}\)C\({}_{4}\) & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The coordinates on the vertexes of edges in the layout template.  
Figure 7: The layout template of the main structure of grounded objects in the galley (7 th building in the Taipei Railway Workshop (TRW) and the size is 84 \(\times\) 38.4 m in Length \(\times\) Width).  
total score of Parent I is 7.26 and Parent II is 7.24. Parent I is a little better than Parent II, and these scores are similar (Figure 8).  
2. Producing offspring layout instance  
The trains were crossed over to produce offspring. The first and second position trains (genes) were exchanged in the layout instance (chromosomes) of Parent I and Parent II, and then Offspring I and Offspring II were produced. The total score of Offspring I is 7.69, and Offspring II is 6.82. Offspring I is higher than Offspring II. Offspring I's score is also better than the original score of Parent I. Simultaneously, the score of Offspring II after crossover is 6.82, which is worse than the original score of Parent II. In other words, the Offspring I is a good option for mutation.  
3. Mutating layout instance  
The third position train (gene) was replaced with the other train. Then, the total score was counted to distinguish and compare its parent and generations. After the mutation of the Offspring I, the score of Offspring I' is 8.3. Offspring I' gains a higher score than Offspring I and gets an optimization result. Then, the train (genes) list of the next generation after mutation was rewritten (Table 9). The calculating process of scores of layout instances for ELP is shown in Algorithm S1.  
As we know, planners' threshold score is the key decision related to the number of layout instances for ELP. In this case, if the threshold score is 7, and then the Parent I, Parent II, Offspring I, and Offspring I' are good options as layout instances. If the threshold score is 7.5, and Offspring I and Offspring I' are good optimizations for ELP. Moreover, if the threshold score is 8, and just Offspring I' is the option as layout instance.