4.6 Article

Multitasking scheduling with batch distribution and due date assignment

期刊

COMPLEX & INTELLIGENT SYSTEMS
卷 7, 期 1, 页码 191-202

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s40747-020-00184-x

关键词

Multitasking; Scheduling; Due date assignment; Batch distribution

资金

  1. National Natural Science Foundation of China [71971041]
  2. Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province [2020JDJQ0035]
  3. Science and Technology Project of EducationDepartment of Jiangxi Province [180375]

向作者/读者索取更多资源

This study focuses on multitasking scheduling problems with batch distribution and due date assignment. The goal is to identify the optimal primary job sequence, job due dates, and batch production strategy in order to minimize total cost. Efficient algorithms are devised and numerical experiments are conducted to evaluate the impact of multitasking on scheduling cost or value.
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.

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