A grouping genetic algorithm for the assembly line balancing problem of sewing lines in garment industry

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 2811 - 2816
• Main Authors: Chen, J.C., Hsaio, M.H., Chun-Chieh Chen, Cheng-Ju Sun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Assembly; Assembly Line Balancing Problem; Biological cells; Clothing industry; Cybernetics; Garment Industry; Genetic algorithms; Grouping Genetic Algorithm; Machine learning; Manufacturing industries; Production; Textile industry; Workstations
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2009.5212600

The garment manufacturing is a traditional and fashion industry, that is globally competitive and customer centric. The most critical operation process is sewing, as it generally involves a great number of operations. The aim of assembly line balancing planning in sewing lines is to assign task to the workstation in order that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines in a reasonable time. This paper presents a grouping genetic algorithm (GGA) for assembly line balancing problem of sewing lines in garment industry. GGA was first developed by Falkenauer in 1992 as a type of GA which exploits the special structure of grouping problem, and overcomes the drawbacks of GA. GGA allocates workload among machines as evenly as possible, so the minimum mean absolute deviations (MAD) can be minimized. The performance is verified through solving two real problems in garment industry. The computational results reveal that GGA outperforms GA in both simple and complex problems by 13.81% and 8.81%, respectively. This shows GGA's effectiveness in solving ALBP.