International Journal of Computational Intelligence Systems

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Volume 9, Issue 6, December 2016, Pages 1082 - 1100

Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

Authors
Jianyou Xua, xujianyou@mail.neu.edu.cn, Win-Chin Linb, linwc@fcu.edu.tw, Junjie Wuc, Wjj58@163.com, Shuenn-Ren Chengd, k0252@gcloud.csu.edu.tw, Zi-Ling Wangb, iloveeat7839@gmail.com, Chin-Chia Wu*b, cchwu@fcu.edu.tw
aCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, China,
bDepartment of Statistics, Feng Chia University, Taichung, Taiwan
cManagement School, Zhejiang Shuren University, Zhejiang, China
dGraduate Institute of Business Administration, Cheng Shiu University, Kaohsiung, Taiwan
Received 14 February 2016, Accepted 18 July 2016, Available Online 1 December 2016.
DOI
10.1080/18756891.2016.1256572How to use a DOI?
Keywords
re-entrant flowshop; learning effect; heuristic-based genetic algorithm
Abstract

Recently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behaviours of all the proposed algorithms when the number of re-entrant times or machine number or learning effect or job size changes.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 6
Pages
1082 - 1100
Publication Date
2016/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1256572How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Jianyou Xu
AU  - Win-Chin Lin
AU  - Junjie Wu
AU  - Shuenn-Ren Cheng
AU  - Zi-Ling Wang
AU  - Chin-Chia Wu*
PY  - 2016
DA  - 2016/12/01
TI  - Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration
JO  - International Journal of Computational Intelligence Systems
SP  - 1082
EP  - 1100
VL  - 9
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1256572
DO  - 10.1080/18756891.2016.1256572
ID  - Xu2016
ER  -