基于GATS混合算法的最优作业切换不相关并行机成组调度研究

不相关并行机调度问题是车间调度中的典型问题,而单件小批量生产模式导致频繁的作业切换和大量的作业切换时间,降低了设备利用率和生产效率。文中提出了基于成组技术的排序依赖作业切换时间的不相关并行机调度问题研究。根据工件加工所需资源的相似性进行工件聚类成组,满足机器约束条件确定所有工件组在各机器上的分配,以及确定同一台机器上的各工件组以及组内的排列顺序。以最小化总拖延时间为优化目标构建了数学模型,应用了遗传禁忌搜索(GATS)算法进行求解,针对不同规模的问题分别对比人工蜂群(ABC)算法和遗传模拟退火(GASA)算法进行案例研究。对比结果显示文中提出的算法具有较好的寻优能力。

Research on group scheduling of optimal setup uncorrelated parallel machine based on GATS hybrid algorithm

The uncorrelated parallel machine schedulNE.Cms_Inserting problem is a typical problem in the workshop scheduling, and the single piece small batch production mode leads to frequent job switching and a large number of setup times, which reduces equipment utilization and production efficiency. This dissertation presents a research on the scheduling of uncorrelated parallel machines based on the grouping technique, which is dependent on the setup time. According to the similarity of the resources required for workpiece processing, the workpieces are clustered and grouped, and with machine constraints condition met, the allocation of all the workpiece groups on the machines as well as the order of the workpiece groups and that within each group on the same machine is determined. In this paper, a mathematical model is constructed with the minimization of total delay time as the optimization goal and genetic tabu search (GATS) algorithm is applied to solve it. Artificial bee colony (ABC) algorithms and genetic simulated annealing (GASA) algorithms are used for case studies. The comparison results show that the proposed algorithm has better searching ability.