TY - JOUR

T1 - Two-stage fuzzy stochastic programming with Value-at-Risk criteria

AU - Wang, Shuming

AU - Watada, Junzo

PY - 2011/1

Y1 - 2011/1

N2 - 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.

AB - 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.

KW - Fuzzy random variable

KW - Fuzzy stochastic programming

KW - Particle swarm optimization

KW - Value of fuzzy random solution

KW - Value of perfect information

KW - Value-at-Risk

UR - http://www.scopus.com/inward/record.url?scp=77957917247&partnerID=8YFLogxK

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U2 - 10.1016/j.asoc.2010.02.004

DO - 10.1016/j.asoc.2010.02.004

M3 - Article

AN - SCOPUS:77957917247

VL - 11

SP - 1044

EP - 1056

JO - Applied Soft Computing

JF - Applied Soft Computing

SN - 1568-4946

IS - 1

ER -