混流装配线排序的改进离散微粒群优化算法

Modified discrete Particle Swarm Optimization algorithm for Mixed model assembly line sequencing

提出在离散空间用一种改进的微粒群优化算法来解决混流装配线的多目标排序问题,考虑两个目标:总生产变化率最小和总闲置-超载时间最小,并对两个目标函数进行了规范化处理以消除量纲的影响。在基本的微粒群算法基础上,提出了适应离散编码的粒子位置编码方式,引入了动态参数方法来提高算法的搜索性能和收敛能力。对实际混流装配线的仿真实验表明提出的改进微粒群优化算法可以直接应用于离散问题并保持了基本微粒群算法的优良性能,是一种性能较好的高效的混流装配线排序算法。

This paper proposed a modified Particle Swarm Optimization （PSO） algorithm to solve the mixed model assembly line sequencing problem in discrete space with two objectives：total production rate variation and total idle-overload time.Compared with the original PSO,this paper presented the encoding method suited for discrete code,and introduced a dynamic parameter scheme to enhance the search ability and the convergence ability.In order to eliminate the effect of dimension,the two objective functions were regulated.The experiments on a mixed model assembly line show that the modified discrete PSO algorithm maintains the advantages of PSO algorithm and can solve the mixed model assembly line sequencing problem effectively and rapidly.