## 4 Discussion  
Multi-product artisanal manufacturing faces operational challenges due to the flexible arrangement of workstations and production variability. Unlike industrial environments with fixed layouts and standardized workflows, these systems require optimization methodologies that not only minimize unnecessary movement but also synchronize human-machine operations to improve overall system efficiency. Traditional layout optimization \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Scenario**} \\ \cline{2-4}  & **2P\(\times\)2W*** & **3P\(\times\)3W*** (Base)** & **4P\(\times\)4W*** * \\ \hline Initial Distance (m) & 654.12 & 868.95 & 1007.98 \\ SLP Distance (m) & 498.92 & 628.24 & 728.75 \\ CRAFT Distance (m) & 442.95 & 533.5 & 628.86 \\ EA Distance (m) & 239.07 & 400.56 & 431.52 \\ \hline \hline \end{tabular}  
* 2P\(\times\)2W: 3 products, 2 workers; 3P\(\times\)3W: 3 products, 3 workers; 4P\(\times\)4W: 4 products, 4 workers.  
\end{table} 
Table 6: Artisanal production scenarios using EA, SLP, and CRAFT. Figure 5: Average time in the system for each product in initial and optimized layouts. approaches, such as SLP and the CRAFT algorithm, have proven effective in structured environments, but their applicability to non-technical businesses remains limited, particularly when dynamic workstation reconfiguration is required. This study implemented an EA programmed in C++ to optimize workstation distribution and synchronize operations in a multi-product wooden furniture workshop, where three types of products are simultaneously manufactured across 16 flexible workstations. The results demonstrate that the EA reduced total worker travel distance by 51.45% (from 868.95 m to 421.87 m) and total system time by 13.2% (from 9284.09 min to 8057.85 min), significantly outperforming conventional methodologies. These findings align with previous studies where layout optimization has proven to reduce costs and travel distances in industrial environments with fixed structures [38]. However, unlike these approaches, the EA applied in this study enables dynamic adjustments, overcoming the limitations of traditional methods in systems with high operational variability. This capability makes it particularly relevant in flexible manufacturing and small-batch production, where layout configurations must continuously adapt to demand. The use of metaheuristics in layout optimization has been widely explored in high-automation environments. [PERSON] and [PERSON] [39] combined an enhanced Genetic Algorithm with SLP, successfully improving cellular layout in injection molding workshops. Similarly, [PERSON] et al. [21] determined that integrating SLP with advanced tools such as GA, Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS) optimizes space allocation and reduces operational costs. However, these approaches do not address dynamic operation synchronization, which is a critical factor in artisanal manufacturing. The present study demonstrates that Evolutionary Algorithms can be successfully applied in such environments, synchronizing tasks in multi-product systems without requiring advanced automation. From a methodological perspective, the stability of the EA depends on key parameter configurations, such as population size, crossover rate, and mutation probability. [PERSON] et al. [30] suggest that integrating an EA with machine learning and hybrid metaheuristics enhances adaptability in environments with high uncertainty. This presents an opportunity to optimize its performance in more complex operational systems with greater demand variability. While this study demonstrates that the EA outperforms traditional methods in reducing travel distances and synchronizing operations in artisanal manufacturing, its scalability and stability should be further explored in larger-scale systems with greater product diversity.