Optimization Of Assembly Complexity In Mixed-model Assembly Systems Based On Human Factors

As the competition becomes even fiercer and customer demands more varied, moreand more companies begin to adopt mix-model assembly production style, which canproduce products with similar structure and technology in one line. Mixed-modelassembly systems satisfy customer with more flexibility, however, making higherrequirements on operators handling assembly complexity. Mixed-model assemblyranges from different product, complicated assembly processes to variety materials,which complicates the operator assembly complexity, causing human error. Therefore,it’s necessary to consider operator reliability when optimizing mixed-model assemblysystems complexity.This paper starts with the performance shaping factors about assembly, classifiesthem, and meanwhile, uses set-valued statistic and aggregation function for verbalevaluation to quantitatively measure operator’s key performance shaping factors.Aiming at optimization of assembly system complexity, operator choice complexity wasstudied. A complexity optimization model based on human factors was developed, andthe impact of complexity-induced disturbance factors on workstation reliability wasanalyzed, which include human fatigue, operator reaction time et al. Mitigate complexityin mixed-model assembly systems and balance complexity among stations using productvariant differentiation, avoiding the situation that the complexity of some workstation istoo high causing the low human reliability and poor product quality.Genetic algorithm and Particle swarm optimization were applied to minimize theassembly system complexity, and MATLAB programming was used for examplevalidation. The two results were compared, which assures the optimization of the results.The results show that this model makes better balance between the workstationcomplexity, and provide reference for manager making line design and product variantdifferentiation decision.