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When the systems under investigation are complex, the analytical solutions to these systems become impossible. Because of the complex stochastic characteristics of the systems, simulation can be used as an analysis tool to predict the performance of an existing system or a design tool to test new systems under varying circumstances. However, simulation is extremely time consuming for most problems of practical interest. As a result, it is impractical to perform any parametric study of system performance, especially for systems with a large parameter space. One approach to overcome this limitation is to develop a simpler model to explain the relationship between the inputs and outputs of the system. Simulation metamodels are increasingly being used in conjunction with the original simulation, to improve the analysis and understanding of decision-making processes. In this study, an artificial neural network (ANN) metamodel is developed for the simulation model of an asynchronous assembly system and an ANN metamodel together with simulated annealing (SA) is used to optimize the buffer sizes in the system.