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, the main purpose of balancing assembly lines is to manufacture products as profitable and as 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, 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 assign tasks in subassembly parts as close to each other as possible. 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). 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.