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CLC number: TH166; TP278

On-line Access: 2019-08-05

Received: 2017-07-09

Revision Accepted: 2018-03-17

Crosschecked: 2019-07-03

Cited: 0

Clicked: 3920

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hai-yan Wang

http://orcid.org/0000-0001-8289-5351

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.7 P.1002-1020

http://doi.org/10.1631/FITEE.1700457


A three-stage method with efficient calculation for lot streaming flow-shop scheduling


Author(s):  Hai-yan Wang, Fu Zhao, Hui-min Gao, John W. Sutherland

Affiliation(s):  College of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314000, China; more

Corresponding email(s):   wanghy@mail.zjxu.edu.cn

Key Words:  Lot streaming, Flow-shop scheduling, Transfer sublots]> Bounded size, Differential evolution


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Hai-yan Wang, Fu Zhao, Hui-min Gao, John W. Sutherland. A three-stage method with efficient calculation for lot streaming flow-shop scheduling[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(7): 1002-1020.

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Abstract: 
An important production planning problem is how to best schedule jobs (or lots) when each job consists of a large number of identical parts. This problem is often approached by breaking each job/lot into sublots (termed lot streaming). When the total number of transfer sublots in lot streaming is large, the computational effort to calculate job completion time can be significant. However, researchers have largely neglected this computation time issue. To provide a practical method for production scheduling for this situation, we propose a method to address the n-job, m-machine, and lot streaming flow-shop scheduling problem. We consider the variable sublot sizes, setup time, and the possibility that transfer sublot sizes may be bounded because of capacity constrained transportation activities. The proposed method has three stages: initial lot splitting, job sequencing optimization with efficient calculation of the makespan/total flow time criterion, and transfer adjustment. Computational experiments are conducted to confirm the effectiveness of the three-stage method. The experiments reveal that relative to results reported on lot streaming problems for five standard datasets, the proposed method saves substantial computation time and provides better solutions, especially for large-size problems.

一种流水车间批量调度的高效计算三阶段优化方法

摘要:在工件含批量生产任务情况下如何进行最佳生产调度是一个重要的生产计划问题。通常将批量工件划分为子批处理(称为分批优化)。若子批数较大,则会大大增加工件完成时间的计算复杂性。现有研究未能考虑此类计算时间问题。本文考虑可变子批、准备时间以及子批批量约束(传输子批批量受传输设备容量限制),提出一种求解n个工件、m台机器流水车间分批优化调度方法。所提方法包含3个阶段:初始批量划分、基于生产周期/总流程时间指标快速评价法的工件排序优化、分批传输方案调整。为验证3阶段优化方法的有效性,采用5个标准数据集进行测试。实验结果表明,所提方法能节省大量计算时间,尤其对大规模问题能提供更优解。

关键词:批量流;流水车间调度;传输子批;可变批量;批量约束;差分进化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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