Proceedings of the 33nd conference on Winter simulation
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Variance-based sampling for cycle time: throughput confidence intervals
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Kriging metamodeling in discrete-event simulation: an overview
WSC '05 Proceedings of the 37th conference on Winter simulation
Design of experiments: overview
Proceedings of the 40th Conference on Winter Simulation
Aggregate simulation modeling of an MRI department using effective process times
Proceedings of the Winter Simulation Conference
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A cycle time-throughput curve quantifies the relationship of average cycle time to throughput rates in a manufacturing system. Moreover, it indicates the asymptotic capacity of a system. Such a curve is used to characterize system performance over a range of start rates. Simulation is a fundamental method for generating such curves since simulation can handle the complexity of real systems with acceptable precision and accuracy. A simulation-based cycle time-throughput curve requires a large amount of simulation output data; the precision and accuracy of a simulated curve may be poor if there is insufficient simulation data. To overcome these problems, sequential simulation experiments based on a nonlinear D-optimal design are suggested. Using the nonlinear shape of the curve, such a design pinpointsp starting design points, and then sequentially ranks the remainingn --p candidate design points, wheren is the total number of possible design points being considered. A model of a semiconductor wafer fabrication facility is used to validate the approach. The sequences of experimental runs generated can be used as references for simulation experimenters.