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One of the more difficult tasks facing a modeler in developing a simulation model of a discrete part manufacturing system is deciding at what level of abstraction to represent the resources of the system. For example, questions about plant capacity can be modeled with a simple model, whereas questions regarding the efficiency of different part scheduling rules can only be answered with a more detailed model. In developing a simulation model, most of the actual features of the system under study must be ignored and an abstraction must be developed. If done correctly, this idealization provides a useful approximation of the real system. Unfortunately, many individuals claim that the process of building a simulation model is an “intuitive art.” The objective of this research is to introduce aspects of “science” to model development by defining quantitative techniques for developing an aggregate simulation model for estimating part cycle time of a manufacturing flow line. The methodology integrates aspects of queueing theory, a recursive algorithm, and simulation to develop the specifications necessary for combining resources of a flow line into a reduced set of aggregation resources. Experimentation shows that developing a simulation model with the aggregation resources results in accurate interval estimates of the average part cycle time.