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

Abstract

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.

A Comprehensive Review: Analysing the Pros and Cons of Assembly Line Balancing Methods

“…ii. Simulated Annealing [11,37,38]: Simulated annealing is based on the process of annealing in metallurgy, in which a metal is heated and then slowly cooled to make it more resistant to stress. This method involves starting with an initial solution and then making random changes to the solution, gradually reducing the magnitude of the changes over time.…”

“…iii. Tabu Search [37,39,40]: Tabu search involves creating a set of forbidden moves or actions, and then using a neighbourhood search process to move from one solution to another. The forbidden moves prevent the algorithm from getting stuck in local optima.…”

“…The advantages and disadvantages of meta-heuristics methods can be listed below, which are extracted from established research journals [10,11,[34][35][36][37][38][39][40][41][42][43].…”

Effects of step-by-step line balancing in apparel assembly line

“…Next, task grouping was performed to assign tasks to workstations. In this study, the Toyota Sewing System, 17 derived from the Toyota Production System for the apparel industry, was adopted. The Toyota Sewing System not only requires less floor space but also offers more flexibility and a better working environment.…”

“…Although the line balancing performance was improved by assigning single tasks with long SAMs to multiple workers, it was limited by single tasks with short SAMs. Chen et al 17 applied the data of Chan et al 15 to a meta-heuristics algorithm and used it to solve the apparel assembly line balancing problem through workers assignment. The authors applied the multiple tasks-single worker assignment method, in which multiple tasks are grouped and assigned to a single worker.…”