面向作业车间调度问题的遗传算法改进

为了获得遗传算法在作业车间调度问题上的最优化解,提高算法的迭代速度,研究了遗传算法的改进方法,以工件的加工时间最短为目标建立调度模型。在算法上提出了基于概率改进的具有自适应能力的交叉与变异算子,以求作业车间调度问题的最优解。在遗传算法上采用精英保留策略方法,并结合改进的自适应算子对问题进行求解。以基准案例LA01和FT06作为实验仿真对象,获得了相应的甘特图以及搜索过程曲线。仿真结果表明,与未改进的算法相比,该算法能够更加快速地获得最优解。改进后的算法在搜索上更加快速有效,在求解作业车间调度问题上具有一定的可行性,更加适合工业加工生产。

Improved genetic algorithm for job shop scheduling

In order to obtain the optimal solution of genetic algorithm for job shop scheduling problem and improve the iteration speed of the algorithm, the improved method of genetic algorithm is studied. The scheduling model is established with the shortest processing time of the workpiece as the target. An adaptive crossover and mutation operator based on probability improvement is proposed to get the optimal solution of the job shop scheduling problem. The elitist retention strategy and the improved adaptive operator are used in the genetic algorithm, to solve solve job shop scheduling problem. The benchmark cases LA01 and FT06 are used as simulation objects. The corresponding Gantt chart and the search process curve are obtained. The simulation results show that the improved algorithm can get the optimal solution more quickly with the unmodified algorithm. The improved algorithm is more efficient and faster. It is feasible to solve job shop scheduling problem, and is more suitable for industrial production.