多工艺路线的批量生产调度优化

以优化生产周期为目标,研究了多工艺路线的批量调度问题,提出了一种基于工序优先级的调度算法,并将该算法嵌入到遗传算法中,得到了全局优化的批量调度算法。遗传算法搜索最佳染色体,调度算法把染色体解码为调度。在调度算法中,采用了3种提高生产率的策略,即区分批量启动时间与工序加工时间,在工件到达机床之前做好准备工作;把一批工件分成多个小生产批次,每批次独立加工:一批工件加工部分后就运向后续加工机床,缩小后续机床的等待时间。仿真表明,该调度方法能取得较好结果。

OPTIMIZATION METHOD FOR A JOB - SHOP SCHEDULING PROBLEM WITH ALTERNATIVE MACHINES IN THE BATCH PROCESS

The job-shop scheduling problem with alternative machines in the batch process is investigated. The heuristic based operation precedence is developed to address the reduction of makespan. Then a new hybrid procedure is presented by combining the heuristic with Genetic algorithms. In the procedure, Genetic algorithms derive the optimal chromosome and the heuristic turns the chromosome into the optimal scheduler. The strategies to improve productivity are three: first, be-fore-arrival setup time is separated from processing times, then the setup is prepared before the job's arrival. Second, the original batch is split into many smaller batches, and every smaller batch is regarded as a single part. Finally, the jobs are transferred to successive machine while a division of batch is finished, so the latency time of the machine is reduced. An example of scheduling is given, and the results show that the method is available and efficient.