After the pilot phase of a simulation study, if the model contains many factors, then direct experimentation may need too much computer processing time, therefore the purpose of screening simulation experiments is to eliminate negligible or unimportant factors of a simulation model in order to concentrate the efforts upon a short list of important factors. For this matter the Sequential bifurcation procedure developed by Bettonvil and Kleijnen [3] is an efficient and effective screening method which can be used. In this study, the Sequential bifurcation screening method is used to determine the important factors of a simulation based decision support model designed by The Swedish Defense Research Agency (FOI) meant for testing operational plans. By using this simulation model, a decision maker is able to test a number of feasible plans against possible courses of events. The sequential bifurcation procedure was applied and sorted the most important factors involved in this simulation model based on their relative importance.

The present project discusses the application of screening techniques in large-scale simulation models with the purpose of determining whether this kind of procedures could be a substitute for or a complement to simulation-based optimization for bottleneck identification and improvement. Based on sensitivity analysis, the screening techniques consist in finding the most important factors in simulation models where there are many factors, in which presumably only a few or some of these factors are important. The screening technique selected to be studied in this project is Sequential Bifurcation. This method consists in grouping the potentially important factors, dividing the groups continuously depending on the response generated from the model of the system under study. The results confirm that the application of the Sequential Bifurcation method can considerably reduce the simulation time because of the number of simulations needed, which decreased compared with the optimization study. Furthermore, by introducing two-factor interactions in the metamodel, the results are more accurate and may even be as accurate as the results from optimization. On the other hand, it has been found that the application of Sequential Bifurcation could become a problem in terms of accuracy when there are many storage buffers in the decision variables list. Due to all of these reasons, the screening techniques cannot be a complete alternative to simulation-based optimization. However, as shown in some initial results, the combination of these two methods could yield a promising roadmap for future research, which is highly recommended by the authors of this project.