Research On Multi-objective Balance Of Mixed Assembly Line Based On Improved Genetic Algorithm

With the progress of science and technology and the rapid development of social economy,the living standard of people also constantly improve,in addition to,the individuation and diversification of demand for the product,makes the car industry continuously benefit continues to rise.In response to this kind of individuation and diversification of demand,most have been adopted by the present domestic automobile manufacturing mixed flow assembly manufacturing mode,but the mixed flow on an assembly line,at the same time assembly structure and process conditions of similar kinds of products,make its complexity is much higher than simple product assembly line.How under existing production conditions,on the improvement of the mixed flow assembly line in a reasonable manner,improve the ability of its products to meet consumer demand for automobile products become various automobile manufacturing enterprise key consideration.Based on the predecessors' research on the assembly line,combining with the current automobile manufacturing industry application assembly line are faced with the problem,put forward to produce beats,workstation operating load and workers manufacturing cost for multiple target optimization of mixed flow assembly line balance,improve product production capacity,reduce workers processing cost,at the same time for the homework load balancing between each workstation,increase the staffs sense of fairness,promote the whole mixed flow assembly line fluency,and the corresponding mathematical model is established.Genetic algorithm is used to solve the complexity of the balance problem of mixed flow assembly line.Genetic algorithm has strong ability to solve such combinatorial optimization problems.However,when solving complex problems,genetic algorithm is prone to fall into the defects of local optimal solution and slow convergence speed.Therefore,the operation of genetic algorithm is improved to improve the performance of genetic algorithm.In this paper,according to the characteristics of the mixed flow assembly line and the defects of genetic algorithm,using the theory of topology generating initial population,elitist selection and the way of combining roulette wheel selection operation,to improve the probability of crossover and mutation,and greedy search method is put forward,in order to improve the optimization ability of the algorithm.To test and verify the effectiveness and generality of improved genetic algorithm,this paper adopts mixed flow assembly line balancing asked in other literature are applied to solve the case results show that the improved genetic algorithm in solving quality has significant improvement.In this paper,the automobile manufacture enterprise real case study,using the improved genetic algorithm to improve automobile mixed flow assembly line,the results show that the proposed equilibrium model of mixed flow assembly line overall balance effect is remarkable,and the improved genetic algorithm to solve practical problems can obtain good result,has practical value.