基于混沌遗传算法的自动化生产单元调度方法

Robotic Cells Scheduling Based on Chaos Genetic Algorithm

针对遗传算法在求解一类带时间窗口的自动化生产单元调度问题时易出现冗余迭代、收敛缓慢等问题,将混沌搜索技术引入至遗传算法中,通过将混沌初始化、混沌扰动与遗传算法的基本操作相结合,利用混沌运动搜索精度高、遍历性好的特点来提高遗传算法的收敛速度和优化质量。本文在给出自动化生产单元调度问题的数学模型的基础上,着重讨论了混沌遗传调度算法的设计,包括编码方式、混沌初始化、交叉操作、混沌变异操作和适应度函数的计算等。最后以自动化电镀生产线为例对提出的算法进行了验证,为此类调度问题提供了有效的算法。

In order to overcome the limitation of redundant iterations and slow convergence when using Genetic Algorithm （GA） to solve robotic production cell scheduling problem with time windows, this paper introduced the chaos search technique into GA by combining the chaos initialization and perturbation with operations of GA. The high precision and good ergodicity of chaos search can increase GA＇s convergence speed and solution quality. After presenting the mathematical model of the robotic cell scheduling problem, this paper discusses the design of the proposed chaos genetic algorithm, including encoding method, chaos initialization, variation operation, crossover operations, and fitness function computing. At last, an example of an automated electroplating line was used to verify the proposed algorithm, which was shown to be effective in solving the considered scheduling problem.