Value-at-Risk-based fuzzy stochastic optimization problems

Shuming Wang*, Junzo Watada

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    Number of pages6
    Publication statusPublished - 2009
    Event2009 IEEE International Conference on Fuzzy Systems - Jeju Island
    Duration: 2009 Aug 202009 Aug 24


    Other2009 IEEE International Conference on Fuzzy Systems
    CityJeju Island


    • Fuzzy random variable
    • Fuzzy stochastic programming
    • Neural network
    • Particle swarm optimization
    • Value-at-Risk

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science


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