A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent

Zheng, Y; Zhang, G; Zhang, Z; Lu, J

A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent

Zheng, Y Zhang, G

Zhang, Z

Lu, J

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Publication Type:: Journal Article
Citation:: Information Sciences, 2018, 454-455 pp. 46 - 58
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Zheng, Y	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Zhang, Z https://orcid.org/0000-0002-6512-1458	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.available	2020-07-02T19:08:37Z
dc.date.issued	2018-07-01	en_US
dc.identifier.citation	Information Sciences, 2018, 454-455 pp. 46 - 58	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129316
dc.description.abstract	© 2018 A weak linear bilevel programming (WLBP) problem often models problems involving hierarchy structure in expert and intelligent systems under the pessimistic point. In the paper, we deal with such a problem. Using the duality theory of linear programming, the WLBP problem is first equivalently transformed into a jointly constrained bilinear programming problem. Then, we show that the resolution of the jointly constrained bilinear programming problem is equivalent to the resolution of a disjoint bilinear programming problem under appropriate assumptions. This may give a possibility to solve the WLBP problem via a single-level disjoint bilinear programming problem. Furthermore, some examples illustrate the solution process and feasibility of the proposed method. Finally, the WLBP problem models a principal-agent problem under the pessimistic point that is also compared with a principal-agent problem under the optimistic point.	en_US
dc.relation.ispartof	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2018.04.045	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent	en_US
dc.type	Journal Article
utslib.citation.volume	454-455	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	454-455	en_US

Abstract:

© 2018 A weak linear bilevel programming (WLBP) problem often models problems involving hierarchy structure in expert and intelligent systems under the pessimistic point. In the paper, we deal with such a problem. Using the duality theory of linear programming, the WLBP problem is first equivalently transformed into a jointly constrained bilinear programming problem. Then, we show that the resolution of the jointly constrained bilinear programming problem is equivalent to the resolution of a disjoint bilinear programming problem under appropriate assumptions. This may give a possibility to solve the WLBP problem via a single-level disjoint bilinear programming problem. Furthermore, some examples illustrate the solution process and feasibility of the proposed method. Finally, the WLBP problem models a principal-agent problem under the pessimistic point that is also compared with a principal-agent problem under the optimistic point.

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http://hdl.handle.net/10453/129316