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Estimating cycle time percentile curves for manufacturing systems via simulation

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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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

Harvard

Yang, F, Ankenman, BE & Nelson, BL 2008, 'Estimating cycle time percentile curves for manufacturing systems via simulation', INFORMS Journal on Computing, vol. 20, no. 4, pp. 628-643. https://doi.org/10.1287/ijoc.1080.0272

APA

Yang, F., Ankenman, B. E., & Nelson, B. L. (2008). Estimating cycle time percentile curves for manufacturing systems via simulation. INFORMS Journal on Computing, 20(4), 628-643. https://doi.org/10.1287/ijoc.1080.0272

Vancouver

Yang F, Ankenman BE, Nelson BL. Estimating cycle time percentile curves for manufacturing systems via simulation. INFORMS Journal on Computing. 2008 Sep;20(4):628-643. doi: 10.1287/ijoc.1080.0272

Author

Yang, Feng ; Ankenman, Bruce E. ; Nelson, B. L. / Estimating cycle time percentile curves for manufacturing systems via simulation. In: INFORMS Journal on Computing. 2008 ; Vol. 20, No. 4. pp. 628-643.

Bibtex

@article{b1615b95456d48fbb843f06a729b4ee0,
title = "Estimating cycle time percentile curves for manufacturing systems via simulation",
abstract = "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.",
keywords = "Discrete Event Simulation , response surface modelling , design of experiments , semiconductor manufacturing, Queueing",
author = "Feng Yang and Ankenman, {Bruce E.} and Nelson, {B. L.}",
year = "2008",
month = sep,
doi = "10.1287/ijoc.1080.0272",
language = "English",
volume = "20",
pages = "628--643",
journal = "INFORMS Journal on Computing",
issn = "1091-9856",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "4",

}

RIS

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 -