基于改进粒子群算法的数据线生产线仿真与优化

Simulation and optimization of data line production line based on improved particle swarm optimization

针对数据线生产线工艺生产过程中所产生的工序不平衡、存在瓶颈工位、产线布局混乱等问题,提出一种基于改进粒子群算法的闭环优化方法。首先,使用仿真软件分析Type-C数据线的生产工艺流程,从而获得其各工序的生产效率;其次,根据生产需求和工作时间得出工位数以及生产节拍,同时基于改进粒子群算法进行工序分配并与传统粒子群算法的优化结果进行比较分析;最后,利用工业工程优化方法对存在的瓶颈工位进行一定的优化。实验结果表明,该算法在此工序分配问题中的平衡效率为95.7%,产量为1588件;与传统粒子群算法相比,改进粒子群算法在平衡效率和生产产量方面分别提高了8.9%和20%。由此可见该方法有助于生产企业在工序分配问题上有效地提高生产效率。

Aiming at the problems of unbalanced process,bottleneck stations,and chaotic station layouts during the process of data line production line,a closed-loop optimization method based on improved particle swarm algorithm was proposed.First,the production process flow of the Type-C data line was analyzed by using simulation software to obtain the production efficiency of each process.Then,the number of stations and production beats were obtained according to production requirements and working time,as well as the process allocation was performed based on the improved particle swarm algorithm and compared with the optimization result of the traditional swarm algorithm.Finally,the existing bottleneck stations were optimized by using industrial engineering optimization methods.The theoretical analysis and simulation results show that the balance efficiency of this algorithm in this process allocation problem is 95.7%,and the output is 1588 pieces.In the comparison experiments with the traditional particle swarm algorithm,the improved particle swarm algorithm increases by 8.9%and 20%in terms of balance efficiency and production output.It can be seen that this method is helpful for production enterprises to effectively improve production efficiency on the issue of process allocation.