Value-at-risk-based fuzzy stochastic optimization problems
Value-at-risk-based fuzzy stochastic optimization problems
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DOI：
10.1109/fuzzy.2009.5277422
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Shuming Wang;J. Watada
中科院分区：
文献类型：
--
作者：
Shuming Wang;J. Watada
本文建立了一类新的模糊随机优化模型--两阶段模糊随机规划模型。结合模糊变量离散化方法、随机模拟技术和二分法，提出了一种计算VaR的近似算法。并证明了逼近算法的收敛性定理。为了求解具有VaR准则的两阶段模糊随机规划问题，我们将近似算法、神经网络和粒子群优化算法相结合，提出了一种混合粒子群优化算法来搜索最优解。最后给出了一个数值例子来说明所设计的混合粒子群算法。
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 two-stage 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.