## 3 Results
### Comparison with Traditional Methods  
The comparative analysis confirms the superiority of the EA over conventional methods such as SLP and CRAFT, particularly in high-variability environments. While SLP and CRAFT are effective in facilities with fixed layouts, the EA enables dynamic adjustments, making it especially useful in artisanal manufacturing, where designs and materials frequently change. The results presented in Table 3 show that the EA reduced the total travel distance by 447.08 m, equivalent to a 51.45% decrease compared to the initial layout, outperforming the reductions achieved by SLP (27.70%) and CRAFT (38.60%). Additionally, the reorganization of workstations reduced the occupied area by 22 m\({}^{2}\), a critical advantage for workshops with spatial constraints. On an individual level, Worker 2, who initially recorded the longest travel distance, reduced their movement from 385.85 m to 175.13 m, demonstrating that the EA improves workstation arrangement and optimizes production sequencing, reducing travel times and maximizing space utilization. Before conducting the ANOVA test, the assumptions of normality, homogeneity of variance, and data independence were verified. The Shapiro-Wilk test was applied to assess normality in the distribution of travel distances for each method, yielding \(p\)-values of 0.09 for EA, 0.12 for CRAFT, and 0.07 for SLP, indicating no sufficient evidence to reject normality. [PERSON]'s test was used to evaluate variance equality across methods, with \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Operator** & **Layout** & **UD** & **ST (h)** & **NU** & **TPU (min)** & **\% BT** & **\% UT** & **TDI (m)** & **DTO (m)** \\ \hline \multirow{2}{*}{Worker 1} & Initial & 1 & 62.68 & 21 & 0.1 & 0 & 0.09 & 123.88 & \\  & Optimized & 1 & 45.57 & 21 & 0.08 & 0.01 & 0.07 & — & 85.37 \\ \hline \multirow{2}{*}{Worker 2} & Initial & 1 & 62.68 & 50 & 0.14 & 0 & 0.2 & 385.85 & \\  & Optimized & 1 & 45.57 & 50 & 0.06 & 0 & 0.13 & — & 162.02 \\ \hline \multirow{2}{*}{Worker 3} & Initial & 1 & 62.68 & 56 & 0.12 & 0 & 0.19 & 359.22 & \\  & Optimized & 1 & 45.57 & 56 & 0.06 & 0 & 0.13 & — & 153.15 \\ \hline \multicolumn{2}{c}{Total Travel Distance} & — & — & — & — & — & 868.95 & 400.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of operator travel distance in initial and EA-optimized layouts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Worker**} & \multicolumn{4}{c}{**Solution Method**} \\ \cline{2-5}  & **ITD \({}^{\ddagger}\)** & **SLP \({}^{\ddagger}\)** & **CAW \({}^{\ddagger}\)** & **EA \({}^{\ddagger}\)** \\ \hline
1 & 123.88 & 114.86 & 84.7 & 85.37 \\
2 & 385.85 & 274.95 & 194.7 & 162.02 \\
3 & 359.22 & 238.43 & 223.3 & 153.15 \\ Total travel distance (m) & 868.95 & 628.24 & 533.5 & 400.56 \\ Distance reduction (m) & — & 240.71 & 335.45 & 468.38 \\ Total travel distance reduction (\%) & — & 27.70 & 38.60 & 53.90 \\ Average execution time per run (s) & 13.5 & 25.2 & 192.00 \\ \hline \hline \end{tabular} \({}^{\dagger}\)ITD: Initial travel distance per worker; \({}^{\ddagger}\) SLP: Systematic Layout Planning; \({}^{\ddagger}\) CAW: CRAFT algorithm in WinQSB\({}^{\ddagger}\); \({}^{\ddagger}\)** E: Evolutionary Algorithm.  
\end{table} 
Table 3: Solution Data for SLP, CRAFT, and EA Methods. a \(p\)-value of 0.25, confirming homoscedasticity. The independence of observations was ensured through controlled iterations without interference between values. The one-way ANOVA test resulted in an F-statistic of 8806.19 and a \(p\)-value < 0.01, indicating significant differences among the evaluated methods (Table 4). These results confirm the effectiveness of the EA in optimizing facility layouts. In terms of computational efficiency, the EA exhibited a higher average execution time per run (192.0 s) compared to traditional methods such as CRAFT (25.2 s) and SLP (13.5 s). However, this longer processing time is compensated by the significant reductions in operator travel distance and overall system time.