Abstract
A new class of two-stage facility location-allocation (FLA) problems is studied, in which the demands are characterized by fuzzy random variables with known possibility and probability distributions. To solve the two-stage FLA problem, an approximation method is developed to turn the original infinite dimensional FLA problem into a finite dimensional one. Since the approximating FLA problem is neither linear nor convex, conventional optimization algorithms cannot be used to solve it. To overcome this difficulty, this paper designs a hybrid algorithm, which integrates the approximation method, neural network (NN) and particle swarm optimization (PSO) algorithm, to solve the approximating two-stage FLA problem. One numerical example with five facilities and ten customers is presented to demonstrate the effectiveness of the designed algorithm.
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