A lambda-cut Approximate Algorithm For Goal-Based Bilevel Risk Management Systems

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dc.contributor.author Gao, Y
dc.contributor.author Zhang, G
dc.contributor.author Lu, J
dc.contributor.author Dillon, TS
dc.contributor.author Zeng, X
dc.date.accessioned 2010-05-28T09:47:21Z
dc.date.issued 2008-01
dc.identifier.citation International Journal of Information Technology & Decision Making (IJITDM), 2008, 7 (4), pp. 589 - 610
dc.identifier.issn 0219-6220
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/9086
dc.description.abstract Bilevel programming techniques are developed for decentralized decision problems with decision makers located in two levels. Both upper and lower decision makers, termed as leader and follower, try to optimize their own objectives in solution procedure but are affected by those of the other levels. When a bilevel decision model is built with fuzzy coefficients and the leader and/or follower have goals for their objectives, we call it fuzzy goal bilevel (FGBL) decision problem. This paper first proposes a lambda-cut set based FGBL model. A programmable lambda-cut approximate algorithm is then presented in detail. Based on this algorithm, a FGBL software system is developed to reach solutions for FGBL decision problems. Finally, two examples are given to illustrate the application of the proposed algorithm
dc.publisher World Scientific Publishing Co., Inc.
dc.relation.isbasedon 10.1142/S0219622008003113
dc.title A lambda-cut Approximate Algorithm For Goal-Based Bilevel Risk Management Systems
dc.type Journal Article
dc.parent International Journal of Information Technology & Decision Making (IJITDM)
dc.journal.volume 4
dc.journal.volume 7
dc.journal.number 4 en_US
dc.publocation USA en_US
dc.identifier.startpage 589 en_US
dc.identifier.endpage 610 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.personcode 001038
dc.personcode 020014
dc.personcode 030567
dc.personcode 102497
dc.percentage 100 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords en_US
pubs.embargo.period Not known
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 Software
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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