Value-at-Risk-based fuzzy stochastic optimization problems

Shuming Wang, Junzo Watada

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Fuzzy Systems
    ページ1402-1407
    ページ数6
    DOI
    出版ステータスPublished - 2009
    イベント2009 IEEE International Conference on Fuzzy Systems - Jeju Island
    継続期間: 2009 8 202009 8 24

    Other

    Other2009 IEEE International Conference on Fuzzy Systems
    CityJeju Island
    Period09/8/2009/8/24

    ASJC Scopus subject areas

    • ソフトウェア
    • 人工知能
    • 応用数学
    • 理論的コンピュータサイエンス

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