Genetic algorithms for assembly line balancing with various objectives
Computers and Industrial Engineering - Special issue: IE in Korea
A heuristic-based genetic algorithm for workload smoothing in assembly lines
Computers and Operations Research
Grouping genetic algorithms: an efficient method to solve the cell formation problem
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Optimization of spreading and cutting sequencing model in garment manufacturing
Computers in Industry
Computers and Industrial Engineering
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
A hybrid grouping genetic algorithm for the registration area planning problem
Computer Communications
A GA methodology for the scheduling of yarn-dyed textile production
Expert Systems with Applications: An International Journal
Evaluating performance advantages of grouping genetic algorithms
Engineering Applications of Artificial Intelligence
An adaptive genetic algorithm with dominated genes for distributed scheduling problems
Expert Systems with Applications: An International Journal
A decision support system for production scheduling in an ion plating cell
Expert Systems with Applications: An International Journal
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.