双资源作业车间智能优化调度

Intelligent optimization for job shop scheduling of dual-resources

用遗传算法研究了双资源作业车间的调度优化问题,提出了一种将归约法与遗传算法和分派规则相结合的调度算法,该算法将机床和工人合理地分配给加工任务(工序),使评价指标获得最优.通过与国外学者的算法进行比较,本算法在相同生产周期的情况下,能够获得平均流动时间较少的调度结果.本算法采用的遗传编码不含工人和机床设备的信息,使得染色体的交叉和变异容易操作,节省了计算时间.最后还就工人/机床设备的比率对作业车间加工性能的影响进行了分析并给出分析结果.

Based on genetic algorithms, dual-resources constrained job-shop-scheduling problem is studied. A new scheduling algorithm is proposed, which combines genetic algorithms and reduction with dispatching rules. It reasonably assigns the resources of machines and works to jobs and achieves optimum on some performance. Compared with the solution suggested by foreign researcher, the proposed algorithm can search better solution on mean flow time. The proposed encoding does not include the information of workers and machines, so it is easily to intercross and mutate between chromosomes and spare computational time. The performance of job shop on worker staffing levels is analyzed and the result is given.