遗传算法求解带权重的生产调度问题

Genetic Algorithm for Solving Weighted Production Scheduling Problem

以优化生产排程,缩短生产周期为目标,研究了多工序的批量调度问题,以遗传算法为基础,提出了一种带有权重的生产调度算法。遗传算法产生初始种群,采用两次编码转换,使得初始种群携带的信息更为完善,将排队时间和运输时间与加工时间分离,根据不同的时间权重得到最为合适的时间组合,使得总的生产周期最小。根据同一机器不同零件和同一零件不同工序分为多种情况,分别得出对应的加工时间和等待时间,根据不同的时间权重,得到全局最优解。结合实例进行仿真实验,结果表明了算法的可行性、准确性。

This paper studies the multi-process batch scheduling problem with the goal of optimizing production schedule andshortening production cycle. It proposes a weighted production scheduling algorithm based on genetic algorithm. The genetic algorithm generates the initial population,and uses two coding conversions to make the information carried by the initial p-opulation more perfect. It separates the queuing time and transportation time from the processing time,and obtains the most suitable time combination according to different time weights,so that the total production cycle is the smallest. According to different parts of the same machine and different processes of the same part,it divides the process into multiple casesand obtained the corresponding processing time and waiting time respectively. It obtaines the global optimal solution according to different time weights. The simulation experiment is carried out with examples. The results show the feasibility andaccuracy of the algorithm.