AN IMMUNE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR MIXED ASSEMBLY LINE BALANCING

Mixed assembly line balancing has been mass used for several decades. With the growing trend of greater product variability and shorter life cycles traditional mass production is being replaced in assembly lines. The current market is intensely competitive and consumer-centric. Mixed-model assembly lines are increasing in many industrial environments. This paper deals with mixed-model assembly line balancing for many models, and uses an immune PSO approach to minimize the number of workstations. The algorithm can overcome the disadvantage of getting into local optimum solution with particle swarm optimization by introducing commutators and commutative orders. To verify its advantage, the proposed algorithm is compared with conventional genetic algorithm through a practical example.