Neural network prediction in a system for optimizing simulations

Neural networks have been widely used for both prediction and classificatio n. Back-propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as simulated annealing, genetic algorithms and tabu search have also been used for this purpose. The developers of these train ing methods, however, have focused on accuracy rather than training speed i n order to assess the merit of new proposals. While speed is not important in settings where training can be done off-line, the situation changes when the neural network must be trained and used on-line. This is the situation when a neural network is used in the context of optimizing a simulation. I n this paper, we describe a training procedure capable of achieving a suffi cient accuracy level within a limited training time. The procedure is first compared with results from the literature. We then use data from the simul ation of a jobshop to compare the performance of the proposed method with s everal training variants from a commercial package.