CLC number: TH166; TP278
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-07-03
Cited: 0
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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.
@article{title="A three-stage method with efficient calculation for lot streaming flow-shop scheduling",
author="Hai-yan Wang, Fu Zhao, Hui-min Gao, John W. Sutherland",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="7",
pages="1002-1020",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700457"
}
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%T A three-stage method with efficient calculation for lot streaming flow-shop scheduling
%A Hai-yan Wang
%A Fu Zhao
%A Hui-min Gao
%A John W. Sutherland
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 7
%P 1002-1020
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700457
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T1 - A three-stage method with efficient calculation for lot streaming flow-shop scheduling
A1 - Hai-yan Wang
A1 - Fu Zhao
A1 - Hui-min Gao
A1 - John W. Sutherland
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 7
SP - 1002
EP - 1020
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700457
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.
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