Fuzzy design structure matrix-guided genetic algorithm for optimizing apparel production line layouts

Purpose The paper endeavors to enhance apparel production efficiency and optimize assembly line layouts by incorporating the inherent constraint relationships of manufacturing processes. By integrating a genetic algorithm (GA) for automated sequencing, a method is introduced that mitigates human intervention and closely matches real-world operations, ultimately bolstering layout and production efficacy. Design/methodology/approach Taking the shirt production as an example, the study quantifies the interdependencies among production components through a fuzzy design structure matrix (FDSM). The derived production sequence of interconnected components from the FDSM informs the optimization of line constraints, incorporating both operational and work cell considerations. This optimization is then fused with a GA for automated line sequencing. Findings In comparison to manual sequencing, the constraint-optimized approach reduces the target objective by 42%, eliminates path loss, elevates the average workstation workload balance by 15.37% and reduces the production machine count by four. Compared to other GA-based optimization studies, the results demonstrate a superior alignment with resource constraints and industrial practices, mirroring actual production conditions more closely. Flexsim simulations affirm that the optimized line outperforms the baseline by generating 11 additional finished products daily, underscoring the efficacy of the process constraint-driven optimization. Originality/value The paper contributes a scientific framework to tackle challenges in apparel production lines, including congestion, workload imbalance, manual layout dependence and inefficient equipment usage. By adopting this approach, apparel enterprises can decrease production costs, optimize resource utilization and attain optimal economic performance.