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A particle swarm optimization based algorithm for fuzzy bilevel decision making with constraints-shared followers
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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents
Honolulu, Hawaii
SESSION: Applications of evolutionary computation track table of contents
Pages 1075-1079  
Year of Publication: 2009
ISBN:978-1-60558-166-8
Authors
Ya Gao  University of Technology, Sydney, NSW, Australia
Guangquan Zhang  University of Technology, Sydney, NSW, Australia
Jie Lu  University of Technology, Sydney, NSW, Australia
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

In a bilevel decision problem, decision making may involve multiple followers and fuzzy demands. This research focuses on the problem of fuzzy linear bilevel decision making with multiple followers who share common constraints (FBCSF). Based on the ranking relationship among fuzzy sets defined by cut set and satisfactory degree α, a FBCSF model is presented and a particle swarm optimization based algorithm is developed. The experiments reveal that solutions obtained by this algorithm are reasonable and stable.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Ya Gao: colleagues
Guangquan Zhang: colleagues
Jie Lu: colleagues