The PCB assembly in the load distribution between the different assembly equipments in SMT production line is an important class of optimization problems in the electronics industry. Given the takt time and the actual production process constraints,its optimization objective is to make different placers load balanced and task allocation optimal,so that improving production efficiency and equipment utilization. Firstly, in order to balance the workload among different placers, based on a variety of different types of the surface mounters, nozzles and component matching particularity, the problem of task allocation optimization of placement machines is proposed; Secondly, the actual production line design information factor parameter components, assembly feasibility, the actual placement time, as well as the placement relationship optimization are analyzed, the mathematical model of load distribution combinatorial optimization is set up given optimization maximizing equilibrium conditions; Finally, aiming at the complexity and particularity of load distribution problems in SMT production line, the mathematical models are optimized through the combination of improved encoding of DNA genetic algorithm to calculate the fitness,and the problem is solved by using MATLAB, and then the optimal solution is found. The results show that:the load distribution optimization method proposed in this paper can effectively solve the problems in load optimization distribution of printed circuit board(PCB)assembly,advance the utilization and equilibrium of equipment,and promote the operation optimization of the production line.
LI Zhi-Gang, WU Hao
. Optimization of Load Distribution on Surface Mount Production Line Based on DNA Genetic Algorithm[J]. Chinese Journal of Management Science, 2016
, 24(10)
: 171
-176
.
DOI: 10.16381/j.cnki.issn1003-207x.2016.10.020
[1] 郭姝娟,靳志宏.表面组装技术生产线贴片机负荷均衡优化[J].计算机集成制造系统,2009,15(4):817-822.
[2] Crama R,Klunder C.Production planning for surface mount technology lines[J].International Journal of Production Research,2004,42(13):2693-2718.
[3] Rogers P,Warrington R.Production planning for surface mount technology lines[J].Discrete Applied Mathematics,2002,123(1):339-341.
[4] 蒋腾旭,谢枫.遗传算法中防止早熟收敛的几种措施[J].计算机与现代化,2006,136(12):54-56.
[5] Tohka J,Krestyannikov E,Toga AW.Genetic algorithms for finite mixture model based voxel classification in neuroimaging[J].IEEE Transactions on Medical Imaging,2007,26(5):696-711.
[6] Deep K,Thakur M.A new crossover operator for real coded genetic algorithms[J].Applied Mathematics and Computation,2007,188(1):895-901.
[7] Adelman L M.Molecular computation of solutions to combinatorial problems[J].Science,1994,266(5187):1021-1024.
[8] Roweis S,Winfree E,Burgoyne R,et al.A sticker-based model for DNA computation[J].Journal of Computational Biology,1998,5(4):615-629.
[9] 丁永生,任立红,邵世煌.DNA遗传算法及其函数寻优应用[C].张嗣赢,王福利:中国控制与决策学术年会论文集.沈阳:控制与决策编辑部,昆明,5月1日,2000:235-239.
[10] 王雷,蒋爱平.基于DNA编码的遗传神经网络算法及应用[J].电子测量与仪器学报,2009,22(6):23-26.
[11] 何洋林,叶春明,马明.装配生产线平衡问题DNA遗传算法研究[J].机械设计与制造,2008,45(3):68-70.
[12] 张友鹏,颜晨阳.一种基于DNA计算的多模态函数求解模型[J].铁道学报,2005,27(6):112-116.
[13] 陈贞.多品种印刷电路板表面贴装生产线优化[D].大连:大连海事大学,2009.
[14] 周开乐,沈超,丁帅,等.基于遗传算法的微电网负荷优化分配[J].中国管理科学,2014,22(3):68-73
[15] 聂书志,叶邦彦.DNA遗传算法在Job Shop调度优化中的应用[J].机械设计与制造,2010,47(5):43-45.
[16] 刘颖,靳志宏.多品种小批量生产环境下表面贴装生产线的平衡优化[J].大连海事大学学报,2012,38(2):87-90.
[17] 李岩.制造系统复杂调度中混合遗传算法的应用[D].上海:上海交通大学,2000
[18] 申哲巍,张树芳,孙东海.改进云遗传算法在负荷优化分配中的应用[J].陕西电力,2012,41(12):43-46.
