Abstract
A cycle time-throughput (CT-TH) curve plays an important role in strategic planning for manufacturing systems. In this research, we seek to quantify the relationship of the moments and percentiles of cycle time to throughput rate via simulation experiments. The estimation of CT-TH moment curves is based on a nonlinear regression metamodel supported by queueing theory, and the adequacy of the model has been proved through our numerical experiments. Utilizing the estimated moment curves of cycle time, we proposed to estimate the CT-TH percentile curves in an indirect method assuming that the underlying distribution of cycle time is a generalized gamma, a highly flexible distribution. More specifically, we fit metamodels for the first three CT-TH moment curves throughout the throughput range of interest, determine the parameters of the generalized gamma by matching moments, and then obtain percentiles by inverting the distribution. To insure efficiency and control estimation error, simulation experiments are sequentially built up to optimize some design criterion.
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