Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Penalty-PSO Algorithm for Sequencing Mixed Model Assembly Line

Authors
Chenglong Lu, Bo Zhu, Beibei Liu, Yuwei Wan
Corresponding Author
Chenglong Lu
Available Online November 2016.
DOI
10.2991/aiie-16.2016.7How to use a DOI?
Keywords
PSO; MMALSP; Penalty Strategy
Abstract

A mathematical model of sequencing problem which takes the minimum overload time as the optimization objective is estab-lished to solve mixed model assembly line sequencing problem in semi-closed station. A new penalty strategy that can eliminate the influence of the viscous effect which caused by the inherent de-fects of the reset strategy is proposed. A Penalty-PSO Algorithm based on penalty strategy has been designed for mixed model assembly line sequencing problem. Experimental results confirm the effectiveness and superiority of Penalty-PSO Algorithm in solving mixed model assembly sequencing problem.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
10.2991/aiie-16.2016.7
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.7How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Chenglong Lu
AU  - Bo Zhu
AU  - Beibei Liu
AU  - Yuwei Wan
PY  - 2016/11
DA  - 2016/11
TI  - Penalty-PSO Algorithm for Sequencing Mixed Model Assembly Line
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 29
EP  - 32
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.7
DO  - 10.2991/aiie-16.2016.7
ID  - Lu2016/11
ER  -