Abstract
With manufacturing cells used more and more commonly in automated factories, a thorough understanding of their operating characteristics is needed. However, a lack of analytical models significantly hinders the development of effective controls system. In this paper, the performance behavior of some manufacturing cells is generalized from the results of extensive simulation runs. Regression metamodels are used to represent cell steady state performance as functions of the cell's independent factors. The dynamic analysis results in a set of linear metamodels for the cell's transient behavior under the influence of machine breakdowns and job changes. The system's first order behavior is further verified for combined effects of these two dynamic events using linear additivity. Since time-consuming simulation is executed off-line, cell operating characteristics can be represented by the metamodels developed to predict cell performance. This will provide valuable and timely information for production control decision making in real time.
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