Research output: Contribution to Journal/Magazine › Journal article › peer-review
Estimating cycle time percentile curves for manufacturing systems via simulation. / Yang, Feng; Ankenman, Bruce E.; Nelson, B. L.
In: INFORMS Journal on Computing, Vol. 20, No. 4, 09.2008, p. 628-643.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Estimating cycle time percentile curves for manufacturing systems via simulation
AU - Yang, Feng
AU - Ankenman, Bruce E.
AU - Nelson, B. L.
PY - 2008/9
Y1 - 2008/9
N2 - Cycle time-throughput (CT-TH) percentile curves quantify the relationship between percentiles of cycle time and factory throughput, and they can play an important role in strategic planning for manufacturing systems. In this paper, a highly flexible distribution, the generalized gamma, is used to represent the underlying distribution of cycle time. To obtain CT-TH percentile curves, we use a factory simulation to 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 obtain any percentile of interest by inverting the distribution. To insure efficiency and control estimation error, simulation experiments are built up sequentially using a multistage procedure. Numerical results are presented to demonstrate the effectiveness of the approach.
AB - Cycle time-throughput (CT-TH) percentile curves quantify the relationship between percentiles of cycle time and factory throughput, and they can play an important role in strategic planning for manufacturing systems. In this paper, a highly flexible distribution, the generalized gamma, is used to represent the underlying distribution of cycle time. To obtain CT-TH percentile curves, we use a factory simulation to 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 obtain any percentile of interest by inverting the distribution. To insure efficiency and control estimation error, simulation experiments are built up sequentially using a multistage procedure. Numerical results are presented to demonstrate the effectiveness of the approach.
KW - Discrete Event Simulation
KW - response surface modelling
KW - design of experiments
KW - semiconductor manufacturing
KW - Queueing
U2 - 10.1287/ijoc.1080.0272
DO - 10.1287/ijoc.1080.0272
M3 - Journal article
VL - 20
SP - 628
EP - 643
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
SN - 1091-9856
IS - 4
ER -