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

Author

Listed:
  • Sungmin Park

    (Department of Industrial Engineering, Arizona State University, Tempe, Arizona 85287-5906)

  • John W. Fowler

    (Department of Industrial Engineering, Arizona State University, Tempe, Arizona 85287-5906)

  • Gerald T. Mackulak

    (Department of Industrial Engineering, Arizona State University, Tempe, Arizona 85287-5906)

  • J. Bert Keats

    (Department of Industrial Engineering, Arizona State University, Tempe, Arizona 85287-5906)

  • W. Matthew Carlyle

    (Department of Industrial Engineering, Arizona State University, Tempe, Arizona 85287-5906)

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.

Suggested Citation

  • Sungmin Park & John W. Fowler & Gerald T. Mackulak & J. Bert Keats & W. Matthew Carlyle, 2002. "D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve," Operations Research, INFORMS, vol. 50(6), pages 981-990, December.
  • Handle: RePEc:inm:oropre:v:50:y:2002:i:6:p:981-990
    DOI: 10.1287/opre.50.6.981.347
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    References listed on IDEAS

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    1. Russell C. H. Cheng & Jack P. C. Kleijnen, 1999. "Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses," Operations Research, INFORMS, vol. 47(5), pages 762-777, October.
    2. George S. Fishman, 1972. "Bias Considerations in Simulation Experiments," Operations Research, INFORMS, vol. 20(4), pages 785-790, August.
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    Cited by:

    1. Feng Yang & Bruce Ankenman & Barry L. Nelson, 2007. "Efficient generation of cycle time‐throughput curves through simulation and metamodeling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(1), pages 78-93, February.
    2. Yang, Feng & Liu, Jingang, 2012. "Simulation-based transfer function modeling for transient analysis of general queueing systems," European Journal of Operational Research, Elsevier, vol. 223(1), pages 150-166.
    3. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    4. P. Pedone & G. Vicario & D. Romano, 2009. "Kriging‐based sequential inspection plans for coordinate measuring machines," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 133-149, March.
    5. J P C Kleijnen & W C M van Beers, 2004. "Application-driven sequential designs for simulation experiments: Kriging metamodelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 876-883, August.
    6. van Beers, Wim C.M. & Kleijnen, Jack P.C., 2008. "Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1099-1113, May.
    7. Cannella, Salvatore & Dominguez, Roberto & Ponte, Borja & Framinan, Jose M., 2018. "Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times," International Journal of Production Economics, Elsevier, vol. 204(C), pages 264-277.
    8. Xiaoqiang Cai & Xianyi Wu & Xian Zhou, 2021. "Optimal unrestricted dynamic stochastic scheduling with partial losses of work due to breakdowns," Annals of Operations Research, Springer, vol. 298(1), pages 43-64, March.
    9. Batur, Demet & Bekki, Jennifer M. & Chen, Xi, 2018. "Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry," European Journal of Operational Research, Elsevier, vol. 264(1), pages 212-224.
    10. Steven M. Brown & Thomas Hanschke & Ingo Meents & Benjamin R. Wheeler & Horst Zisgen, 2010. "Queueing Model Improves IBM's Semiconductor Capacity and Lead-Time Management," Interfaces, INFORMS, vol. 40(5), pages 397-407, October.
    11. Feng Yang & Bruce E. Ankenman & Barry L. Nelson, 2008. "Estimating Cycle Time Percentile Curves for Manufacturing Systems via Simulation," INFORMS Journal on Computing, INFORMS, vol. 20(4), pages 628-643, November.

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