Abstract: With the dynamic changes such as different customer needs,equipment updates and improvements in assembly process and technology,the assembly line needed to be constantly adjusted,even balanced again.Evolution balance factors were researched for Mixed-Model Assembly Line (MMAL) in many ways,and a method was proposed for MMAL evolution balancing.A balance mathematical model,in which the minimum cycle time,smoothness index between workstation stations and adjustment costs were as the optimization target was established for MMAL evolution and optimized through an Improved Particle Swarm Optimization (IPSO) algorithm simultaneously.To increase particle diversity and improve search speed,the inertia factor was updated by using the success rate of particle evolution in IPSO,in which the favorable information in non-optimal particles was added to the optimal particle in the group.The effectiveness and feasibility of the method were validated by optimizing assembly line balancing of a manufacturing enterprise.

Key words: mixed assembly line, evolution balancing, particle swarm optimization, optimization

摘要： 随着产品需求的多样化、装配工艺及技术进步、设备更新等动态变化,装配线平衡方案需不断调整,甚至重新规划与演进平衡。为了探究上述因素对混流装配线演进平衡的影响,提出了实现装配线演进平衡的方法,建立了以最小化装配线的生产节拍、站间平滑指数、演进平衡调整成本为优化目标的混流装配线演进平衡数学模型,并通过改进粒子群优化算法进行优化。在该算法中,为增加粒子的多样性和搜索能力,克服传统粒子群优化算法快速收敛等问题,以粒子进化的成功率来更新算法中的惯性因子,将群体中非最优粒子中的有利信息迁移到群体中的最优粒子上,从而加快算法的搜索速度。结合某企业的生产实例验证了该方法的有效性和可行性。

关键词: 混流装配线, 演进平衡, 粒子群优化算法, 优化