Research on optimization of balance and sequencing of mixed-model assembly lines in simulation environment

In view of the problems of long queuing time, low assembly efficiency and failure to meet the order needs of customers arising from the unbalanced station load in the process of mixed-model assembly of wind power brake in an enterprise, on the basis of parallel consideration of the interaction between balance and sequencing of mixed-model assembly lines, a multi-objective optimization model is established to minimize the maximum completion time and the station load index. An improved Fruit Fly Optimization Algorithm based on Pareto is proposed. The hybrid parallel coding is composed of three parts: the number of stations, the type of stations, and the feeding order of the loading frame. Multi-group strategy, adaptive step size, and elite retention strategy are introduced to ensure that good individuals are not destroyed. Based on the example analysis and data basis and through Python language development environment, the algorithm program was developed to obtain the new balancing and sorting scheme and the balancing and sorting scheme for the simple genetic algorithm. The optimal balancing and sorting scheme of the above algorithms were simulated on the Discrete System Simulation Software - Plant Simulation to verify the effectiveness of the algorithm.