Uncapacitated plant location-allocation problems with price sensitive stochastic demands
Computers and Operations Research
Hybrid evolutionary method for obstacle location-allocation
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Swarm intelligence
A class of fuzzy random optimization: expected value models
Information Sciences: an International Journal
On minimum-risk problems in fuzzy random decision systems
Computers and Operations Research
Fuzzy random programming with equilibrium chance constraints
Information Sciences—Informatics and Computer Science: An International Journal
Measurability criteria for fuzzy random vectors
Fuzzy Optimization and Decision Making
Uncertainty Theory
Convergence criteria and convergence relations for sequences of fuzzy random variables
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
The Approximation Method for Two-Stage Fuzzy Random Programming With Recourse
IEEE Transactions on Fuzzy Systems
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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.