摘要
不同品种作业元素的作业时间差异经常引起混合品种装配线的工作站瞬时负荷瓶颈问题,依据给定的排产顺序,兼顾装配线平均负荷和瞬时负荷,考虑不同品种作业元素的作业时间差异对装配线平衡的影响,建立了以最小化工作站内装配时间波动、工作站负荷平滑指数及装配线超载时间为目标的混合品种装配线平衡模型,并设计了基于非支配排序的粒子群优化算法(NSPSO)。实例验证表明,基于非支配排序的粒子群算法在求解大规模混合品种平衡问题方面比遗传算法具有更高的求解质量和求解效率。
The station instantaneous workload bottleneck was caused by task processing time differences among different models. According to the given production schedule and considering the average workload and instantaneous load of assembly line, mixed assembly line balancing model was built with the objective of minimum station processing time variation, station＇s workload smooth index and assembly line overload time. The effect of task processing time differences among different models on the assembly line balance was also taken in to account. The muhi-objective optimization algorithm based on non-dominated sorting particle swarm optimization （NSPSO） was designed. The example verified that, compared with genetic algorithm, NSPSO had higher solution quality and solving efficiency in large mixed balancing problem.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2013年第10期248-252,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(51175304)
关键词
混合品种装配线
平衡
多目标优化
非支配排序
粒子群算法
Mixed assembly line
Balance
Muhi-objective optimization
Non-dominated sorting
Particle swarm algorithm
