基于概率选择交叉和自适应交叉变异的柔性作业车间调度

FLEXIBLE JOB SHOP SCHEDULING BASED ON PROBABILITY SELECTION CROSSOVER AND ADAPTIVE CROSSOVER VARIATION

对于柔性作业车间的调度问题,本文将传统的遗传算法当中的部分内容进行改进来求解。建立优化模型,其中以完工时间作为目标,并且提出一种基于概率选择的多邻域搜索的交叉协同操作方式和自适应交叉变异的方法。这种方法可以扩大算法局部搜索的能力,有效防止所提出算法陷入局部情况下的最优。通过最终结果表明,所提出的改进算法大大减少产品总的加工时间,从侧面证明了所提出的算法是真实有效的。

An improved genetic algorithm(IGA)is proposed to solve the flexible shop scheduling problem.A scheduling model with the finish time as the optimization objective is established.A cross cooperative operation mode and adaptive cross mutation method based on probability selection in multi-neighborhood search are designed.This method can strengthen the local search ability and effectively avoid the algorithm falling into the local optimal.The experimental results show that the improved algorithm can effectively shorten the total processing time of products,and prove the effectiveness of the algorithm.