摘要
针对简单遗传算法在解决作业车间调度问题时只适用于简单问题的局限,研究了多工艺路线的批量调度遗传算法实现,论述了3种提高生产效率的调度策略,即采用最小批量原则对零件进行分批调度生产;将批量准备时间和零件加工时间相分离,在工件到达加工机床前做好批量加工准备;在生产加工过程中,将同批加工零件进行多次机床间转移,缩短后续机床的等待时间。同时将工序优先级调度算法加入到简单遗传算法,提出了一种全局优化的多工艺路线批量生产调度混合遗传算法。仿真结果表明,该调度算法能取得较好的效果。
For the problems resolved with Sample Genetic Algorithm （SGA） are too simple, the job-shop scheduling problem （JSP） with alternative machines in the batch process is investigated. Three strategies to improve productivity are discussed., first, the original batch is split into many smaller batches, and every smaller batch is regarded as a single part. Second, the before-arrival setup time is separated from processing times, then the setup is prepared before the job＇s arrival. Finally, the jobs are transferred to successive machine while a division of batch is finished, so the latency time of the machine is reduced. Simultaneity, a new hybrid procedure is presented by combining the hcuristic with Simple Genetic Algorithms. An example of scheduling is given, and the results show that the method is available and efficient.
出处
《计算机与现代化》
2007年第1期52-55,70,共5页
Computer and Modernization
关键词
作业调度
批量生产
作业车间调度
遗传算法
production scheduling
hatch process
job-shop scheduling
genetic algorithm
