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It is clear that methods are mixed in practice. Problems don't come labelled as simulation, optimisation, forecasting, or with some other methodological name. In practice, there's a job to be done and the analyst must find a way to do it. For over 20 years, optimisation within discrete simulations has been a fertile field of research. Employing time series methods to analyse simulation output and to model input data is routine. Thus, in one sense, we should not be too exercised by the very idea that methods are usefully mixed in research either. Climbing to a higher level, it is likely to be rare that major decisions are made solely on the basis of a few simulation runs. A model is likely to be one element of a decision making process that leads people to see that a particular course of action is either desirable, or less undesirable than alternatives.