Abstract: To improve the efficiency of optimization via simulation (OvS), an OvS method based on neural network is proposed. Taking advantage of the approximation ability of neural network to nonlinear input-output relationship, neural network's outputs are used as substitutes for simulation results to reduce the required simulation runs. Samples are generated by simulation according to the three proposed samples selection methods. Owning to its advantages on learning speed, network stability and parameters selection, generalized regression neural network (GRNN) is adopted to train the samples. The trained GRNN forms a regression surface that represents the relationship between simulation inputs and outputs, which makes it feasible to use GRNN output as substitutes for simulation runs. Optimization algorithms are applied to search for the best solution on the regression surface. Experiments are carried out with some typical test functions, and the feasibility and effectiveness of our method are demonstrated.

Key words: neural network, optimization via simulation, regression surface, samples selection