Fuzzy supply chain problem with VaR criteria

This paper attempts to present a new class of fuzzy two-stage supply chain problem with minimum risk criteria in the sense of value-at-risk (VaR), in which the transportation cost coefficients and the demands are characterized by fuzzy variables with known possibility distributions. Since the fuzzy parameters has infinite supports, the conventional optimization algorithms cannot be used directly to solve the proposed fuzzy supply chain problem. To overcome this difficulty, an approximation method is developed to turn the original supply chain problem into a finite dimensional one. Since approximating is a time-consuming process, we design a hybrid algorithm by integrating approximation method, neural network (NN) and particle swarm optimization (PSO) to solve it. Finally, one numerical example is presented to demonstrate the effectiveness of the designed algorithm.