Research On Balance Optimization Of C Company Energy Storage Battery Assembly Line Based On Particle Swarm Genetic Hybrid Algorithm

Under the background of energy transition,insufficient production capacity has become one of the main contradictions faced by major new energy enterprises under the situation of expanding market demand.New energy enterprise C company through the continuous enhancement of product core competitiveness,its energy storage batteries in short supply.Therefore,to improve the balance of assembly line becomes the key to improve production capacity and economic benefits.In recent years,the application of intelligent optimization algorithm to solve the assembly line balance problem has become a research hotspot at home and abroad.Aiming at the balance problem of energy storage battery assembly line of C Company,this paper proposes the balance optimization scheme of integrated particle swarm genetic hybrid algorithm and industrial engineering method.Effectively improve the balance rate of the assembly line,balance the operating load,and improve the daily production capacity.Finally,Flexsim software was used to verify the effectiveness of the optimization scheme.Firstly,the stopwatch is used to study and measure the working time of each process,and the assembly line balance evaluation index is obtained,and the problems of low assembly line balance rate and high smoothness index are found.Then,the cause of the problem is analyzed.Secondly,the assembly process is optimized by program analysis,and the bottleneck time is reduced by two-handed job analysis.On this basis,a mathematical model constrained by the job priority relation of assembly line was established,and a new algorithm,particle swarm genetic hybrid algorithm,was designed.The genetic operators such as selection,crossover and mutation were embedded into the particle swarm optimization algorithm for fusion.Under the condition that the 20 workstations remain unchanged,the workstations are reorganized to seek the optimization objective of the maximum assembly balance rate and the lowest smoothness index,and the MATLAB software is used to solve it.By comparing the optimization results of particle swarm genetic hybrid algorithm(PSO-GA),particle swarm optimization(PSO)and genetic algorithm(GA),the convergence of the hybrid algorithm and the global optimization effect are proved to be better.Finally,by comparing the output data of Flexsim simulation model before and after optimization,it is verified that the optimization scheme can effectively improve the load rate of each workstation,reduce the idle rate and increase the daily energy.At the same time,the8 S management and pull production management are put forward to further consolidate the optimization results.The research shows that after the optimization of the balance scheme,the processes of the assembly line are reduced from 49 to 48,the number of workstations is reduced from 21 to 20,the assembly beat is reduced from 890.34 seconds to 786.60 seconds,and the total operation time is reduced from 14196.73 seconds to 13999.79.The balance rate increased from 75.93% to 89.00%,the smoothness index decreased from 240.06 to 98.58,and the daily output increased from 65 to 74.