In this paper, we establish a stochastic grouping scheduling model. In the model, there is no installation time between the jobs in the same group, but each group has an installation time before processing. There are learning effects between groups and within groups, and the completion time of jobs is a random variable. We take the long expected schedule and the expected total completion time as the objective function, and the noninterruptible static priority strategy is obtained. At the same time, heuristic algorithms and examples are given.

中文翻译：

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具有学习效果和随机处理时间的分组调度

在本文中，我们建立了一个随机分组调度模型。在该模型中，同一组的作业之间没有安装时间，但每个组在处理之前都有一个安装时间。组间和组内都存在学习效应，作业完成时间是一个随机变量。我们以长预期进度和预期总完成时间为目标函数，得到不可中断的静态优先策略。同时给出了启发式算法和实例。