Applying Meta-Heuristics Algorithm to Solve Assembly Line Balancing Problem with Labor Skill Level in Garment Industry

• Main Authors: Gary Yu-Hsin Chen, Ping-Shun Chen, Jr-Fong Dang, Sung-Lien Kang, Li-Jen Cheng
• Format: Article
• Language: English
• Published: Atlantis Press 2021-04-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Assembly line balancing problem; Toyota sewing system; Tabu search; Simulated annealing
• Online Access: https://www.atlantis-press.com/article/125955859/view

This research investigates how to properly place garment industry workers to work stations in the assembly line to achieve a more balanced production and to reduce the production cycle time. We simulate the assembly line balancing problem via staff assignments. In our research, we conduct a comparative case study and implement our own simulation. The experiments are designed with both single- and multitasking modes. Each experiment is carried out for 10 runs. Finally, we compare our results obtained among constructive greedy, tabu search and simulated annealing. We find that tabu search algorithm is better than simulated annealing on the problem of staff assignment. Meanwhile, we also observe that if we adjust 30% labor force from single task into multitasking mode, the assembly line performance deteriorates. This case is accentuated for workers with disparate skill levels for different tasks.