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