摘要
针对生产节拍确定条件下以提高装配线平衡程度为目的的装配线平衡问题,将装配线平滑系数和装配线平衡率作为优化目标,考虑装配作业分配、工作站数量等因素,使用线性加权和法,以两个优化目标的优先占比作为权重参数建立单目标装配线平衡优化模型;对遗传算法(Genetic Algorithm,GA)和蚁群(Ant Colony Optimization,ACO)算法的混合算法进行改进,构造新的适应度函数和距离信息矩阵对模型进行求解;最后对经典算例进行数值实验,实验结果与以往算法结果比较,平衡程度改进均值提高了5%,表明改进的模型及算法可以更好地提高装配线的平衡程度,验证了模型及算法的有效性。
To solve the problem of assembly line balance problem with a determined production cycle and improve the balance de-gree of assembly line,a single-objective assembly line balance optimization model was established.According to the different pri-ority weighting rate of optimization goals,the linear weight sum method was used to establish the model.The optimization goals of the model are the assembly line smoothing index and assembly line balance rate,considering the assembly job allocation,the number of workstations and other factors.The hybrid algorithm of Genetic Algorithm(GA)and Ant Colony Optimization(ACO)algorithm for solving the model was improved.The new fitness function and distance information matrix were constructed.Finally,numerical experiments are carried out on the classical examples.The experimental results show that the im-proved model and algorithm can improve the balance of the assembly line to a greater extent and verify the effectiveness of the model and algorithm.
作者
景湉佳
贾世会
迟晓妮
唐秋华
JING Tianjia;JIA Shihui;CHI Xiaoni;TANG Qiuhua(College of Science,Wuhan University of Science and Technology,Wuhan 430081,China;Hubei Provincial Key Laboratory of Systems Science in Metallurgical Process,Wuhan University of Science and Technology,Wuhan 430081,China;School of Mathematics and Computing Science,Guilin University of Electronic Technology,Guilin 541004,China;School of Machinery and Automation,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《现代制造工程》
CSCD
北大核心
2024年第3期8-14,22,共8页
Modern Manufacturing Engineering
基金
国家自然科学基金资助项目(12361064,11901068)
湖北省冶金工业过程系统科学重点实验室开放基金项目(Z202301)
广西自然科学基金项目(2021GXNSFAA220034)。
关键词
装配线平衡问题
遗传算法
蚁群算法
单目标优化
assembly line balancing problem
genetic algorithm
ant colony optimization algorithm
simple objective optimization
