What is it about?
The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is important for the apparel industry to be flexible enough to respond quickly to the unstable customers’ demand through continuous improvement of their process efficiency and productivity. This study proposed assembly line balancing problem (ALBP) for complex garment assembly line using simulation-based optimization under stochastic task times. The proposed ALBP solution approach aimed at minimizing the cycle time for a given number of workstations with consideration of constraints on number of resources, precedence relations, and resource types. The empirical study was conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 69 workstations was considered. The discrete event simulation of the trouser assembly line was developed using Arena simulation software. The local optimal solution was obtained from simulation experiments which was adopted for the optimization process. The OptQuest tool was used to solve a single objective optimization with discrete control values.
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Why is it important?
The results showed that the average throughput increased by 30% for local optimal line balancing and 55% for global optimal line balancing. Consequently, the cycle time reduced by 23% and 36%, respectively
Perspectives
This article was prepared from my master thesis. It was through enormous input from my supervisors. This paper contributes to a state-of-the-art assembly line balancing in garment manufacturing. I hope this paper improves the current practice of line balancing in Apparel industry to reduce the cycle time as well as optimize the production throughput. This article advances the industrial engineering application in Apparel industry
Eng. Ocident Bongomin
Moi University
Read the Original
This page is a summary of: A complex garment assembly line balancing using simulation‐based optimization, Engineering Reports, September 2020, Wiley,
DOI: 10.1002/eng2.12258.
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