This study addresses the multitasking scheduling problems with batch distribution and due date assignment (DDA). Compared with classical scheduling problems with due date-related optimization functions, the job due dates are decision variables rather than given parameters. The jobs completed are distributed in batches, and the sizes of all batches are identical, which may be bounded or unbounded. The jobs in every batch are scheduled one by one. Each batch incurs a fixed cost. Under multitasking environment, it allows the machine to put an uncompleted job on hold and turn to another uncompleted job. The goal is to identify the optimal primary job sequence, the optimal job due dates, and the optimal batch production and distribution strategy so that one of the following two optimization functions is minimised: the total cost composed of the earliness penalty, DDA cost, tardiness penalty and batch distribution cost, and the total cost composed of the earliness penalty, weighted number of late jobs, DDA cost and batch distribution cost. We devise efficient exact algorithms for the problems we consider, and perform numerical experiments to check how multitasking affects the scheduling cost or value, the results of which can assist decision-makers to justify the extent to put to use or refrain from multitasking.

这项研究解决了批量分配和到期日分配（DDA）的多任务调度问题。与具有与到期日相关的优化功能的经典调度问题相比，作业到期日是决策变量，而不是给定参数。已完成的作业是分批分发的，并且所有批处理的大小都是相同的，可以有界也可以无界。每一批中的作业都是一个一个地安排的。每批次产生固定成本。在多任务环境下，它允许机器暂挂未完成的作业，然后转到另一个未完成的作业。目标是确定最佳的主要作业顺序，最佳的作业到期日期以及最佳的批生产和分配策略，以使以下两个优化功能之一最小化：总成本包括尽早罚款，DDA成本，拖延罚款和批次分配成本，总成本包括尽早罚款，延迟工作的加权数量，DDA成本和批次分配成本。我们针对所考虑的问题设计了有效的精确算法，并进行了数值实验，以检查多任务处理如何影响调度成本或价值，其结果可以帮助决策者证明使用或避免多任务处理的合理性。