摘要
AGV(automatic guided vehicle)作为物料运输的载体,使生产线物料按节拍流动,是产线设计的关键部分。针对产线初步规划方案中AGV的数量、配送量及速度参数未确定影响产线最佳方案设计的问题,将产线产品生产周期、暂存区总容量、设备平均利用率及AGV平均利用率作为多个目标。采用全因子试验方法,探究AGV的数量、配送量及速度3个因素对多个目标的影响及其变化规律,确定关键因子与优化目标。建立多目标数学模型,采用遗传算法求解,调整并行工序数量,获得优化方案并仿真验证。研究结果表明:该方法能够有效求解问题,使作业的AGV数量减少至1辆,提高资源利用率,降低企业投资成本。
Automatic guided vehicle as the carrier of material transportation,make the production line material flow according to time,which is a key part of the production line design.Aiming at the problem that the AGV quantity,distribution quantity and speed parameters are not determined in the preliminary scheme of production line,which affects the optimal scheme of production line.The production cycle of production line,total capacity of temporary storage area of production line,average utilization rate of equipment and average utilization rate of AGV were taken as multi-objective.Full factor experiment method was used to study the influence of AGV quantity,distribution quantity and speed on multiple targets and its changing rules,identify key factors and optimization targets.A multi-objective mathematical model was established,and genetic algorithm was used to solve the problem,and adjust the number of parallel processes,the optimization scheme was obtained and verified by simulation.The results show that the method can solve the problem effectively,and reduce the number of working AGVs to 1,it improves resource utilization and reduces the investment cost of enterprises.
作者
贺田龙
邵明国
白晓庆
曹泽
李艳鹏
HE Tianlong;SHAO Mingguo;BAI Xiaoqing;CAO Ze;LI Yanpeng(CRRC Yongji Electric Co.,Ltd.,Yongji 044502,CHN;Shanxi Key Laboratory of Traction Motor for Rail Transit,Yongji 044502,CHN)
出处
《制造技术与机床》
北大核心
2022年第11期177-182,共6页
Manufacturing Technology & Machine Tool
关键词
生产线
试验设计
关键因子
遗传算法
多目标优化
production line
experiment design
key factor
genetic algorithm
multi-objective optimization
