Particle Swarm Optimization Based Assembly Line Balance And Simulation Research Of Company A

After China entered the 21 st century,the home appliance manufacturing industry has developed very rapidly in China,and air conditioners have become one of the most important household appliance commodities.This mass-produced product is assembled from various parts,and the balance rate of the assembly line determines the production efficiency and economic benefits of the enterprise.The application of intelligent optimization algorithm in the assembly line balance problem has been relatively common.This paper takes the air-conditioning assembly line of company A as an example to study,and improves and optimizes it through the improved particle swarm algorithm.In this paper,the actual inspection of the air-conditioning assembly line of company A is carried out,and various methods used by domestic and foreign researchers to solve the problem of assembly line balance are found.After systematic analysis,the existing problems of the air-conditioning assembly line are found.First,use ECRS principles,5W1 H and other traditional IE methods to find bottleneck processes,and use program analysis,layout analysis,and man-machine operation analysis to improve them.Secondly,using particle swarm optimization as a research method,the genetic algorithm and simulated annealing algorithm are combined with particle swarm optimization to obtain genetic particle swarm optimization(GA-PSO)and simulated annealing particle swarm optimization(SA-PSO).Comparing simulated annealing particle swarm optimization with basic particle swarm optimization and genetic particle swarm optimization,it is proved that SA-PSO has faster convergence speed and stronger optimization ability,thus verifying the superiority of the algorithm.Therefore,SA-PSO is used to optimize the air-conditioning assembly line of Company A.In this paper,the number of workstations is used as the decision variable,and the optimized search is carried out by the simulated annealing particle swarm algorithm.Finally,it is concluded that when the number of workstations is reduced to 11,the optimization effect is optimal,and the balance rate of the assembly line is increased from50.1% to 78.8%.,the smoothness index decreased from 36.20 to 11.27,and the optimization effect was significantly improved.Finally,the Flexsim simulation software is used to model and simulate the assembly line before and after the improvement.By comparing the utilization rate of each workstation on the assembly line before and after the improvement,the effectiveness of the optimization results is verified.