The Scheduling of Flexible Manufacturing System Based on RBF Neural Network
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Improving the procedure management for flexible manufacturing system (FMS) is still one of the main topics in present industries. In this paper, a radial basis function neural network (RBF-NN) model is proposed to schedule jobs in a general FMS. First, a FMS and the problem are described. Then, a RBF-NN is presented; the iterative training algorithm employs the gradient algorithm to minimize a function that measures the difference between the network output and the desired one. Finally, according to some scheduling rules the ideal configuration of the RBF-NN that is used for the criterion of mean tardiness is figured out. The ideal configuration of the RBF-NN has 8 input nodes, 17 nodes in the hidden layer, and 13 nodes in the output layer. Simulation results show that the combinations are the optimal strategies.
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