基于DNA遗传算法的表面贴装生产线负荷优化分配

Optimization of Load Distribution on Surface Mount Production Line Based on DNA Genetic Algorithm

印制电路板组装任务的负荷优化分配包含设备约束、工艺约束等大量约束,是电子行业表面贴装生产线中的一类重要优化问题。其优化目标是在生产节拍给定和一定约束条件下,使得不同贴装机负荷均衡,任务分配达到最优。首先,根据不同表面贴装机、不同吸嘴及多种类型元件匹配的的复杂性,提出贴装机任务分配组合优化的问题;然后分析设备和元件的参数、组装可行性、贴装时间,以及贴装优化关系等因素,并提出假设条件,建立了平衡率最大化条件下的负荷分配组合优化的数学模型;最后,针对贴装生产线负荷分配问题的复杂性与特殊性,通过改良编码方式后的DNA遗传算法来优化组合数学模型,计算适应度,并借助MATLAB进行仿真求解,进而找到最优解。结果表明:本文提出的贴装生产线负荷分配方法可以解决带复杂约束的印制电路板组装负荷优化分配问题,提高设备的平衡率和生产效率,促进生产线的优化运行。

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.