Searching for important factors: Sequential bifurcation under uncertainty

The problem of searching for important factors in a simulation model is considered when the simulation output is subject to stochastic variation. Bettonvil and Kleijnen (1996) give a method which they call sequential bifurcation which allows a large number factors to be considered using a relatively small number of simulation runs. They give the method under the assumption that the simulation response contains negligible random error, and show that when the number of important factors is small then the method is effective and efficient. In this paper the method is extended to handle simulations where the response is stochastic and subject to significant error. An attraction of the sequential bifurcation method is its flexibility in exploring the effects of different factors. The approach in this paper is to develop a clear but flexible framework in which the method is used as an exploratory tool. For illustration a numerical example is considered using a simulation metamodel involving 24 factors. The example is quite a testing one because the different factors cover a spread of values of differing importance. The results show that the method is capable of handling such situations.