基于元胞机和改进GA的大型零件柔性作业车间调度算法

Flexible Job-Shop Scheduling Algorithm of Large Parts Based on Cellular Automata and Improved GA

针对大型零件柔性作业车间调度问题,采用改进遗传算法优化元胞机局部演化规则,提出了元胞机和改进遗传算法相结合的混合调度算法。依据总加工时间最短、各工位负荷率高、同一工位组各工位负荷平衡率高的优化目标,建立了离散化后单个静态调度单元的遗传算法优化模型,并结合算例具体说明了优化过程。通过文献实例演算验证了混合算法求解大型零件柔性作业车间调度问题的可行性和有效性。

According to the flexible job--shop scheduling problem of large parts, the local evolution rule of cellular automata was optimized using an improved GA and a hybrid scheduling algorithm combining cellular automata with improved GA was proposed. Based on three optimization objectives of minimizing the total processing time, maximizing load rates of all the work stations and maximizing balance rate of a group of work stations of the same type, A GA optimization model for all the static scheduling units was built, and the optimization process was explained by use of a numerical example. The feasibility and efficiency of the hybrid scheduling algorithm of cellular automata and improved GA was verified through applying it into actual example referenced from literature.