Balancing The Shirt Production Line Under Different Operational Constraints Using An Integer Programming Model

Efficient use of capacity is significant to enable apparel businesses to work cost-effectively and provide timely service to their customers. The increase in assembly-line efficiency is associated with lower operating costs. Therefore, balancing assembly lines is mainly to manufacture products as profitable and quickly as possible. In this study, we consider a single-model assembly line balancing problem with workforce and machine constraints in the sewing department of an apparel company. We develop an integer programming (IP) model to optimally balance the shirt production line, considering parallel machines in each stage of the line and various operational constraints such as cycle time and precedence constraints, task machine eligibility, and the number of operators available. The IP model can either minimize the number of open workstations or both, minimize the number of open workstations and simultaneously assign tasks in subassembly parts close to each other. The model has been run under various scenarios using LINGO 15.0 optimization software. Additionally, we have balanced the shirt production line using the Ranked Positional Weight Method (RPWM) for comparison purposes. The IP model outperforms the RPWM results across all scenarios and finds 33 stations and 86.8% efficiency compared to 38 stations and 75.4% balance efficiency with the RPWM.

Automated summary

This study focuses on the single-model assembly line balancing problem in the sewing department of an apparel company. An integer programming model is developed to optimize the balance of the shirt production line. The results show that the IP model outperforms the Ranked Positional Weight Method in improving production efficiency and reducing the number of workstations.