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    A λ-cut approximate algorithm for goal-based bilevel risk management systems

    116390_10029_PUB-CBS-EEB-MC-47282.pdf (876.5Kb)
    Access Status
    Open access
    Authors
    Gao, Y.
    Zhang, G.
    Lu, J.
    Dillon, Tharam S.
    Zeng, X.
    Date
    2008
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Gao, Ya and Zhang, Guangquan and Lu, Jie and Dillon, Tharam and Zeng, Xianyi. 2008. A λ-cut approximate algorithm for goal-based bilevel risk management systems. International Journal of Information Technology and Decision Making. 7 (4): pp. 589-610.
    Source Title
    International Journal of Information Technology and Decision Making
    DOI
    10.1142/S0219622008003113
    ISSN
    02196220
    Faculty
    Curtin Business School
    School of Information Systems
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    Electronic version of an article published as International Journal of Information Technology and Decision Making, Volume 7, Issue 4, 2008, pp. 589-610. DOI: 10.1142/S0219622008003113. Copyright © 2008 World Scientific Publishing Co. All rights reserved. Journal URL: http://ideas.repec.org/s/wsi/ijitdm.html.

    URI
    http://hdl.handle.net/20.500.11937/12766
    Collection
    • Curtin Research Publications
    Abstract

    Bilevel programming techniques a re developed for decentralised 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 codlicients 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 A-cut set based FGBL model. A programmable A-CUt approximate algorithm is then presented in detail. Based on this algorithm, a FCBL software system is developed to reach solutions for FGBL decision problems. Finally, two examples are given to illustrate the application of the proposed algorithm.

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