Multi-objective Balancing Optimization And Simulation Of Mixed-flow Assembly Line Based On Improved Genetic Algorithm

With the advent of the 21 st century,the level of science and technology has been greatly improved,people's material demand has been greatly improved,and the number of products must be able to satisfy consumers,which makes enterprises need to continuously innovate technology And increasing production can provide consumers with a wide range of products with strong characteristics,so as to achieve a win-win situation for consumers and enterprises.In order to achieve this goal,many companies have adopted mixed-flow assembly lines,which can process or assemble different types of products at the same time,and need to establish a complete production planning system,which is far more difficult and complex than a single type of product assembly line.On the already-fixed assembly line,how to improve the assembly line to increase the production capacity of the product is a major problem facing the enterprise.On the basis of the balance optimization of the existing mixed-flow assembly line,comprehensive consideration is given to reducing the cost of the enterprise,improving the ability of the assembly line to process products,and improving the fluency of the assembly line to process products.The goal is to minimize the number of worksites in the mixed-flow assembly line,the actual production cycle,and the processing cost of the product,and to optimize the work load balance among the worksites,and establish the corresponding multi-objective mathematical model.Aiming at the problem of high difficulty coefficient of balance of mixed assembly line,this paper selects a wide range and effective genetic algorithm to solve the multi-objective combinatorial optimization problem as the solution method.Genetic algorithm has a strong ability to solve multi-objective combinatorial optimization problems,but it has problems such as slow convergence speed and easy to fall into local optimal solutions.Therefore,this article makes corresponding improvements to the genetic algorithm to improve the ability to solve mixed assembly lines,uses topological sorting theory and random search methods to generate initial populations,changes the single roulette selection operation,and avoids chromosomes with high fitness values from being screened out.Combine the elite selection method with roulette to improve crossover and mutation operations and set corresponding probabilities.In order to verify the effectiveness of the improved algorithm,a case in the literature is selected to solve the problem.The results show that the improved genetic algorithm has significantly improved ability to solve the problem.In the Flexsim 3D simulation software,a simulation model corresponding to the actual mixed-flow assembly line is established,and the corresponding entities in the simulation model are parameterized.Analyzing the obtained assembly line status report,the results show that the utilization rate of each work of the mixed assembly line has been significantly improved,the output has increased,and the backlog of products in the temporary storage area is less.The assembly process is more coherent,indicating that the improved genetic algorithm is effective in solving the multi-objective balance optimization problem.