An approximation Kuhn-Tucker approach for fuzzy linear bilevel decision making

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dc.contributor.author Zhang, Guangquan en_US
dc.contributor.author Lu, Jie en_US
dc.contributor.author Dillon, Tharam en_US
dc.contributor.editor Lakhmi Jain, Gloria Wren en_US
dc.date.accessioned 2010-05-28T09:38:15Z
dc.date.available 2010-05-28T09:38:15Z
dc.date.issued 2008 en_US
dc.identifier 2006010631 en_US
dc.identifier.citation Zhang Guangquan, Lu Jie, and Dillon Tharam 2008, 'An approximation Kuhn-Tucker approach for fuzzy linear bilevel decision making', in http://dx.doi.org/10.1007/978-3-540-76829-6_6 (ed.), Springer, The Netherlands, pp. 157-171. en_US
dc.identifier.issn 978-3-540-76828-9 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/7856
dc.description.abstract In bilevel decision making, the leader aims to achieve an optimal solution by considering the follower's optimized strategy to react each of his/her possible decisions. In a real-world bilevel decision environment, uncertainty must be considered when modeling the objective functions and constraints of the leader and the follower. Following our previous work, this chapter proposes a fuzzy bilevel decision making model to describe bilevel decision making under uncertainty. After giving the definitions of optimal solutions and related theorems for fuzzy bilevel decision problems this chapter develops an approximation Kuhn?Tucker approach to solve the problem. Finally, an example of reverse logistics management illustrates the application of this proposed fuzzy bilevel decision making approach. en_US
dc.publisher Springer en_US
dc.relation.isbasedon http://dx.doi.org/10.1007/978-3-540-76829-6_6 en_US
dc.title An approximation Kuhn-Tucker approach for fuzzy linear bilevel decision making en_US
dc.parent Intelligent Decision Making en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 157 en_US
dc.identifier.endpage 171 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 080108 en_US
dc.personcode 020014 en_US
dc.personcode 001038 en_US
dc.personcode 030567 en_US
dc.percentage 100 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 030567 en_US


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