Designing simultaneous simulation experiments

Simulation experiments are often designed assuming that a fixed (and known) computing budget is to be allocated sequentially among different alternatives. However, in actual simulation experiments, there may be budget uncertainty or at least flexibility-for example, when there is a soft deadline for obtaining the study results. In such situations, it may be beneficial to allocate resources simultaneously in dynamically changing proportions. In this paper, we examine optimal resource allocation paths. These paths climb the contour curves of the probability of selecting the best of several alternatives in a manner that insures that the highest probability of correct selection, P(CS), is obtained when the study is halted. To gain insight into the complexity of optimal resource allocation paths, simple models exhibiting serial correlation, cross-correlation and trends are studied