Home > Research > Publications & Outputs > Metamodeling for cycle time-throughput-product ...

Research

Links

Text available via DOI:

Keywords

View graph of relations

Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting

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

Published

Standard

Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting. / Yang, Feng; Liu, Jingang; Tongarlak, Mustafa et al.
In: Production Planning and Control, Vol. 22, No. 1, 2011, p. 50-68.

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

Harvard

Yang, F, Liu, J, Tongarlak, M, Ankenman, BE & Nelson, BL 2011, 'Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting', Production Planning and Control, vol. 22, no. 1, pp. 50-68. https://doi.org/10.1080/09537287.2010.490026

APA

Yang, F., Liu, J., Tongarlak, M., Ankenman, B. E., & Nelson, B. L. (2011). Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting. Production Planning and Control, 22(1), 50-68. https://doi.org/10.1080/09537287.2010.490026

Vancouver

Yang F, Liu J, Tongarlak M, Ankenman BE, Nelson BL. Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting. Production Planning and Control. 2011;22(1):50-68. doi: 10.1080/09537287.2010.490026

Author

Yang, Feng ; Liu, Jingang ; Tongarlak, Mustafa et al. / Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting. In: Production Planning and Control. 2011 ; Vol. 22, No. 1. pp. 50-68.

Bibtex

@article{c580aec6875a4e54a5dc292182b2ddad,
title = "Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting",
abstract = "A simulation-based methodology is proposed to map the mean of steady-state cycle time (CT) as a function of throughput (TH) and product mix (PM) for manufacturing systems. Nonlinear regression models motivated by queueing analysis are assumed for the underlying response surface. To ensure efficiency and control estimation error, simulation experiments are built up sequentially using a multi-stage procedure to collect data for fitting the models. The resulting response surface is able to provide a CT estimate for any TH and any PM, and thus allows the decision maker to instantly investigate options and trade offs regarding their production planning.",
keywords = "discrete event simulation , response surface modelling , design of experiments , queueing , semiconductor manufacturing",
author = "Feng Yang and Jingang Liu and Mustafa Tongarlak and Ankenman, {Bruce E.} and Nelson, {Barry L.}",
year = "2011",
doi = "10.1080/09537287.2010.490026",
language = "English",
volume = "22",
pages = "50--68",
journal = "Production Planning and Control",
issn = "0953-7287",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting

AU - Yang, Feng

AU - Liu, Jingang

AU - Tongarlak, Mustafa

AU - Ankenman, Bruce E.

AU - Nelson, Barry L.

PY - 2011

Y1 - 2011

N2 - A simulation-based methodology is proposed to map the mean of steady-state cycle time (CT) as a function of throughput (TH) and product mix (PM) for manufacturing systems. Nonlinear regression models motivated by queueing analysis are assumed for the underlying response surface. To ensure efficiency and control estimation error, simulation experiments are built up sequentially using a multi-stage procedure to collect data for fitting the models. The resulting response surface is able to provide a CT estimate for any TH and any PM, and thus allows the decision maker to instantly investigate options and trade offs regarding their production planning.

AB - A simulation-based methodology is proposed to map the mean of steady-state cycle time (CT) as a function of throughput (TH) and product mix (PM) for manufacturing systems. Nonlinear regression models motivated by queueing analysis are assumed for the underlying response surface. To ensure efficiency and control estimation error, simulation experiments are built up sequentially using a multi-stage procedure to collect data for fitting the models. The resulting response surface is able to provide a CT estimate for any TH and any PM, and thus allows the decision maker to instantly investigate options and trade offs regarding their production planning.

KW - discrete event simulation

KW - response surface modelling

KW - design of experiments

KW - queueing

KW - semiconductor manufacturing

U2 - 10.1080/09537287.2010.490026

DO - 10.1080/09537287.2010.490026

M3 - Journal article

VL - 22

SP - 50

EP - 68

JO - Production Planning and Control

JF - Production Planning and Control

SN - 0953-7287

IS - 1

ER -