International Journal of Computational Intelligence Systems

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Volume 9, Issue 5, September 2016, Pages 917 - 931

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Authors
Hongwei Gehwge@dlut.edu.cn, Liang Sun*, liangsun@dlut.edu.cn, Xin Chenxinchendut@dlut.edu.cn, Yanchun Liangycliang@jlu.edu.cn
College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023, China
Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, Missouri, 63130, USA
College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023, China
College of Computer Science and Technology, Dalian University of Technology, Dalian, 116023, China
College of Computer Science and Technology, Jilin University, Changchun, 130012, China
*Corresponding author: College of Computer Science and Technology, Dalian University of Technology, Dalian, China. Tel: 86-41186980422, Email: liangsun@dlut.edu.cn
Corresponding Author
Received 5 November 2015, Accepted 27 May 2016, Available Online 1 September 2016.
DOI
10.1080/18756891.2016.1237190How to use a DOI?
Keywords
Flexible job shop scheduling; artificial fish swarm model; estimation of distribution; Friedman test and Holm procedure
Abstract

The flexible job shop scheduling problem (FJSP) is one of the most important problems in the field of production scheduling, which is the abstract of some practical production processes. It is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation sequence. In this paper, an efficient artificial fish swarm model with estimation of distribution (AFSA-ED) is proposed for the FJSP with the objective of minimizing the makespan. Firstly, a pre-principle and a post-principle arranging mechanism that operate by adjusting machine assignment and operation sequence with different orders are designed to enhance the diversity of population. Following this, the population is divided into two sub-populations and then two arranging mechanisms are applied. In AFSA-ED, a preying behavior based on estimation of distribution is proposed to improve the performance of algorithm. Moreover, an attracting behavior is proposed to improve the global exploration ability and a public factor based critical path search strategy is proposed to enhance the local exploitation ability. Simulated experiments are carried on BRdata, BCdata and HUdata benchmark sets. The computational results validate the performance of the proposed algorithm in solving the FJSP, as compared with some other state of the art algorithms.

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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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 5
Pages
917 - 931
Publication Date
2016/09/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1237190How 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  - Hongwei Ge
AU  - Liang Sun
AU  - Xin Chen
AU  - Yanchun Liang
PY  - 2016
DA  - 2016/09/01
TI  - An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling
JO  - International Journal of Computational Intelligence Systems
SP  - 917
EP  - 931
VL  - 9
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1237190
DO  - 10.1080/18756891.2016.1237190
ID  - Ge2016
ER  -