## 5 Conclusions  
Mexican artisanal manufacturing is characterized by family-based management and the lack of formal records in its production processes, which has limited its study from a scientific perspective. This study addressed the facility layout problem in a workshop specializing in wooden furniture manufacturing. This is typically an environment marked by limited space, shared workstations, and simultaneous multi-product elaboration without predefined sequential scheduling. These conditions make layout optimization in this sector a complex challenge, difficult to resolve using conventional methodologies.  
To tackle this issue, an EA was implemented to optimize workstation distribution and synchronize operations. The results indicate that the EA reduced total worker travel distance by 51.45% (from 868.95 m to 421.87 m) and decreased total system time by 13.2% (from 9284.09 min to 8057.85 min), significantly outperforming traditional methodologies such as SLP and CRAFT. Additionally, space redistribution reduced the occupied area by 22 m\({}^{2}\), improving internal mobility and reducing downtime. On an individual level, Worker 2 reduced their travel distance by 54.6% (from 385.85 m to 175.13 m), while Worker 3 decreased their walking by 55.8% (from 359.22 m to 158.63 m), demonstrating the EA's impact on workflow optimization in artisanal manufacturing.  
Despite these advancements, certain limitations were identified in the model's implementation. The EA's sensitivity to parameter calibration, such as population size, crossover rate, and mutation probability, can affect solution stability and quality. Additionally, its applicability in larger-scale production systems with greater product diversity remains an open area for further exploration to ensure its scalability and robustness in manufacturing environments with spatial and technological constraints.  
Given these considerations, future research should focus on applying the EA to other manufacturing models, such as cellular manufacturing within the artisanal wooden furniture sector, to distribute and organize workstations into specialized production cells. This transition could optimize workflow without compromising system flexibility, adapting to multi-product elaboration and the need for dynamic adjustments in workstation and material layout. Combining EA with multi-objective optimization models and machine-learning-based heuristics could enable real-time workstation reconfiguration, adjusting to demand variations and spatial constraints. Furthermore, the integration of digital twins would facilitate model validation in real-world implementation, minimizing operational risks and enhancing process control in artisanal manufacturing.  
Finally, further EA optimization should explore the incorporation of material handling and transportation costs, leading to a more comprehensive optimization of the total production system. This approach would not only minimize worker travel distances but also reduce internal logistics costs, strengthening the competitiveness of artisanal workshops compared to more technologically advanced production models.  
Writing (Original Draft), E.F.-E., G.F.-L. and L.E.G.-S.; Writing (Review and Editing), L.R.-S., J.M.C.-D. and J.A.-U.; Data Curation, J.A.-U. and R.A.M.-A.; Methodology and Validation, G.F.-L., R.A.M.-A. and L.E.G.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in this article.  
The authors express their gratitude to the Group of Artisanal Wooden Furniture Industry Enterprises of Misantla, Veracruz, Mexico, for their valuable support and collaboration in facilitating the development of this research.  
The authors declare no conflicts of interest.