Abstract
Apparel manufacturing is a operator-intensive traditional industry helping the economy growth in Taiwan. The most critical manufacturing process is sewing, as it generally involves a great number of manual operations. A balanced sewing line can reduce operator requirement, increase production efficiency, decrease operator cost, and reduce production cycle time. This paper uses Grouping Genetic Algorithm (GGA) to solve types I and II Resource-Constrained Assembly Line and Worker Assignment Balancing Problem (RCALWABP) in sewing lines of apparel industry. Type I RCALWABP in sewing lines was solved using GGA to minimize the number of workstations for a given cycle time. Type II RCALWABP was solved using GGA to minimize the cycle time and maximize the throughput for a given number of workstations. Type II RCALWABP is generally considered as the extension of Type I RCALWABP. The solution of types I and II RCALWABP in sewing lines has high practical value, but there is only limited literature in this area. This paper takes into account several practical characteristics in apparel industry, including multi-skill operators, operator efficiency, and learning curve. Real data from apparel factories will be used to set the best parameters of GGA and evaluate GGA’s performance based on experimental design.