<p>Using surrogate approximations (e.g. Kriging interpolation or artifical neural networks) is an established technique for decreasing the execution time of simulation optimization problems. However, constructing surrogate approximations can be impossible when facing complex simulation inputs, and instead one is forced to use a surrogate model, which explicitly attempts to simulate the inner workings of the underlying simulation model. This dissertation has investigated if postprocessing the output of a surrogate model with an artificial neural network can increase its accuracy and value in simulation optimization problems. Results indicate that the technique has potential in that when output post-processing was enabled the accuracy of the surrogate model increased, i.e. its output more losely matched the output of the real simulation model. No apparent improvement in optimization performance could be observed however. It was speculated that this was due to either the optimization algorithm used not taking advantage of the improved accuracy of the surrogate model, or the fact the the improved accuracy of the surrogate model was to small to make any measurable impact. Further investigation of these issues must be conducted in order to get a better understanding of the pros and cons of the technique.</p>

Using surrogate approximations (e.g. Kriging interpolation or artifical neural networks) is an established technique for decreasing the execution time of simulation optimization problems. However, constructing surrogate approximations can be impossible when facing complex simulation inputs, and instead one is forced to use a surrogate model, which explicitly attempts to simulate the inner workings of the underlying simulation model. This dissertation has investigated if postprocessing the output of a surrogate model with an artificial neural network can increase its accuracy and value in simulation optimization problems. Results indicate that the technique has potential in that when output post-processing was enabled the accuracy of the surrogate model increased, i.e. its output more losely matched the output of the real simulation model. No apparent improvement in optimization performance could be observed however. It was speculated that this was due to either the optimization algorithm used not taking advantage of the improved accuracy of the surrogate model, or the fact the the improved accuracy of the surrogate model was to small to make any measurable impact. Further investigation of these issues must be conducted in order to get a better understanding of the pros and cons of the technique.