Two-stage fuzzy stochastic programming with Value-at-Risk criteria
Two-stage fuzzy stochastic programming with Value-at-Risk criteria
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DOI：
10.1016/j.asoc.2010.02.004
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发表时间：
2011
期刊：
影响因子：
1.2
通讯作者：
Shuming Wang;J. Watada
中科院分区：
文献类型：
--
作者：
Shuming Wang;J. Watada
建立了一类新的模糊随机优化模型--带风险值准则的两阶段模糊随机规划(FSP-VaR)。讨论了两阶段FSP-VaR的一些性质，如完全信息值(VPI)、模糊随机解(VFRS)值以及模糊随机解的界。将模糊变量离散化方法、随机模拟技术和二分法相结合，提出了一种计算VaR的近似算法。证明了该近似算法的收敛性。针对两阶段FSP-VaR问题，提出了一种基于变异邻域的混合粒子群算法(MN-PSO)来搜索近似最优解。此外，还讨论了一种基于神经网络的加速方法。数值实验表明了该混合MN-PSO算法的有效性。比较结果表明，混合MN-PSO算法在性能上优于其他方法，如混合PSO和GA。
A new class of fuzzy stochastic optimization models—two-stage fuzzy stochastic programming with Value-at-Risk (FSP-VaR) criteria is built in this paper. Some properties of the two-stage FSP-VaR, such as value of perfect information (VPI), value of fuzzy random solution (VFRS), and bounds of the fuzzy random solution, are discussed. An Approximation Algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence of the approximation algorithm is proved. To solve the two-stage FSP-VaR, a hybrid mutation-neighborhood-based particle swarm optimization (MN-PSO) which comprises the Approximation Algorithm is proposed to search for the approximate optimal solution. Furthermore, a neural network-based acceleration method is discussed. A numerical experiment illustrates the effectiveness of the proposed hybrid MN-PSO algorithm. The comparison shows that the hybrid MN-PSO exhibits better performance than the one when using other approaches such as hybrid PSO and GA.