Abstract:

Objective In order to adapt to the transformation and upgrading of clothing manufacturing enterprises, and quickly respond to market demand, most clothing manufacturing enterprises had introduced many intelligent production equipment such as clothing suspension systems to replace conventional backward bundled assembly line production. In order to apply new production equipment in the actual production, some problems were identified for the mixed assembly line in the clothing manufacturing workshop such as unreasonable suspension system material transmission path, time consumption and energy consumption, uneven workload time of each workbench, long processing cycle, and waste of processing equipment resources. The optimization of workshop assembly line layout considering the collaboration of clothing suspension systems was carried out aiming to effectively solve the above-mentioned problems of clothing production lines.

Method Because of the involvement of multiple disciplines such as clothing, logistics, and operations research, as well as the complexity of the production process, it was necessary to conduct on-site investigations, observe the production line process of clothing production, analyze the production process diagram of clothing production enterprises and the raw data in MES systems. Based on the constraints of the clothing production process, limited workbench resources and processing equipment quantity, and rational production rhythm, a dual objective mathematical programming model was established to minimize material transmission distance and smoothness coefficient. The non-dominant sorting genetic(NSGA-II) algorithm was designed and applied to solve the production lines of multi-style clothing.

Results From the optimization objective iteration curve generated using NSGA-II algorithm, it was found that the algorithm had a good optimization effect on the green mixed clothing assembly line, and the total distance and smoothness coefficient of material transmission had converged to a stable level in over 300 generations. Based on the production data examples, the Pareto optimal solution set was obtained. Because of the two objectives of the total distance of material transmission and the smoothing coefficient, a non-dominated solution with the best total distance and the non-dominated solution with the best smoothing coefficient were extracted from the solution set for analysis. When 12 workstations are operational and 21 processing devices are utilized, the average idle time per workstation amount to 42.2 s, resulting in an optimal total distance of material transport measuring 82 m. However, the smoothness coefficient stands at 55.86 and the compilation efficiency is merely 75.2%. Conversely, when operating with only 10 workstations and utilizing 18 processing devices, each workstation experiences an average idle time of approximately 16.6 s while achieving a smoother coefficient of 24.104. This leads to an improved compilation efficiency of up to 90.2%, albeit at the expense of increased material transport distance reaching a value of approximately 100 m. The total distance of material transmission in the minimum production cycle was 82 m, with a smoothness coefficient of 55.86, and 12 workstations were needed with 21 processing equipment. The average idle time of each workbench was 42.2 s, and the staffing efficiency was 75.2%. The total transmission distance of the material was 100 m, and the smoothness coefficient was 21.977. It was necessary to operate 10 workstations with 18 processing equipment. The idle time of each workbench was 16.6 s, and the staffing efficiency was 90.2%. The Gantt chart of the work tasks for each workstation in the mixed clothing assembly line were generated according to the scheme. In order to verify the effectiveness of the model, NSGA-II algorithm and multi-objective particle swarm optimization(MOPSO) algorithm were compared, and the result showed that NSGA-II algorithm produced closer simulation results.

Conclusion Faced with the transformation of the clothing industry structure, the design of the entire production line plays a crucial role in intelligent clothing manufacturing. The optimization of workshop assembly line layout considering the synergistic effect of clothing suspension system helps promote the transformation and it is useful for upgrading of China's clothing and textile industry from labor-intensive to less labor intensive or unmanned production. This study provides some theoretical reference for the promotion of green and intelligent manufacturing in the clothing enterprises.

Key words: two-objective layout optimization, clothing hanging, clothing workshop, mixing assembly line, NSGA -Ⅱ algorithm, energy saving and efficiency improvement