D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve

TLDR

A cycle time‑throughput curve describes how average cycle time varies with throughput, indicating a system’s asymptotic capacity and performance across start rates, and is typically generated by simulation, which requires substantial output data for precision. The study proposes sequential simulation experiments using a nonlinear D‑optimal design to address the data‑intensity problem. The design selects p initial points based on the curve’s nonlinear shape and then sequentially ranks the remaining n–p candidate points, validated on a semiconductor wafer fabrication model. The resulting experimental sequences serve as useful references for simulation experimenters.

Abstract

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 pinpoints p starting design points, and then sequentially ranks the remaining n – p candidate design points, where n 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.