Assembly line balancing in garment industry

Garment manufacturing is a traditional industry with global competition. The most critical manufacturing process is sewing, as it generally involves a great number of operations. The aim of assembly line balance planning in sewing lines is to assign tasks to the workstations, so that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines within a reasonable time.This paper develops a grouping genetic algorithm (GGA) for ALBP of sewing lines with different labor skill levels in garment industry. GGA can allocate workload among machines as evenly as possible for different labor skill levels, so the mean absolute deviations (MAD) can be minimized. Real data from garment factories and experimental design are used to evaluate GGA’s performance. Production managers can use the research results to quickly design sewing lines for important targets such as short cycle time and high labor utilization.

► A grouping genetic algorithm was proposed for assembly line balancing problem in garment industry.
► The sewing line in garment industry has different labor skill levels.
► This algorithm maximizes the workload smoothness between workstations.
► A real garment factory is used as a case study for performance evaluation.
► The proposed algorithm outperforms current methods used in practice.