An extended kth-best approach for referential-uncooperative bilevel multi-follower decision making

Zhang, G; Shi, C; Lu, J

An extended kth-best approach for referential-uncooperative bilevel multi-follower decision making

Zhang, G

Shi, C Lu, J

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Publication Type:: Journal Article
Citation:: International Journal of Computational Intelligence Systems, 2008, 1 (3), pp. 205 - 214
Issue Date:: 2008-08-01

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Field	Value	Language
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Shi, C	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2008-08-01	en_US
dc.identifier.citation	International Journal of Computational Intelligence Systems, 2008, 1 (3), pp. 205 - 214	en_US
dc.identifier.issn	1875-6891	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9102
dc.description.abstract	Bilevel decision techniques have been mainly developed for solving decentralized management problems with decision makers in a hierarchical organization. When multiple followers are involved in a bilevel decision problem, called a bilevel multi-follower (BLMF) decision problem, the leader's decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. The referential-uncooperative situation is one of the popular cases of BLMF decision problems where these multiple followers don't share decision variables with each other but may take others' decisions as references to their decisions. This paper presents a model for the referential-uncooperative BLMF decision problem. As the kth-best approach is one of the most successful approaches in dealing with normal bilevel decision problems, this paper then proposes an extended kth-best approach to solve the referential-uncooperative BLMF problem. Finally an example of logistics planning illustrates the application of the proposed extended kth-best approach.	en_US
dc.relation.ispartof	International Journal of Computational Intelligence Systems	en_US
dc.relation.isbasedon	10.2991/ijcis.2008.1.3.2	en_US
dc.title	An extended kth-best approach for referential-uncooperative bilevel multi-follower decision making	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	1	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Bilevel decision techniques have been mainly developed for solving decentralized management problems with decision makers in a hierarchical organization. When multiple followers are involved in a bilevel decision problem, called a bilevel multi-follower (BLMF) decision problem, the leader's decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. The referential-uncooperative situation is one of the popular cases of BLMF decision problems where these multiple followers don't share decision variables with each other but may take others' decisions as references to their decisions. This paper presents a model for the referential-uncooperative BLMF decision problem. As the kth-best approach is one of the most successful approaches in dealing with normal bilevel decision problems, this paper then proposes an extended kth-best approach to solve the referential-uncooperative BLMF problem. Finally an example of logistics planning illustrates the application of the proposed extended kth-best approach.

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http://hdl.handle.net/10453/9102