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Queue Length Forecasting in Complex Manufacturing Job Shops

Author

Listed:
  • Marvin Carl May

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Alexander Albers

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Marc David Fischer

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Florian Mayerhofer

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Louis Schäfer

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Gisela Lanza

    (wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany)

Abstract

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

Suggested Citation

  • Marvin Carl May & Alexander Albers & Marc David Fischer & Florian Mayerhofer & Louis Schäfer & Gisela Lanza, 2021. "Queue Length Forecasting in Complex Manufacturing Job Shops," Forecasting, MDPI, vol. 3(2), pages 1-17, May.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:21-338:d:552197
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    References listed on IDEAS

    as
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    2. Wu, Kan & McGinnis, Leon, 2012. "Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization," European Journal of Operational Research, Elsevier, vol. 221(2), pages 328-339.
    3. Junliang Wang & Jie Zhang, 2016. "Big data analytics for forecasting cycle time in semiconductor wafer fabrication system," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7231-7244, December.
    4. Debjit Roy, 2016. "Semi-open queuing networks: a review of stochastic models, solution methods and new research areas," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1735-1752, March.
    5. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    6. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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    Cited by:

    1. Marvin Carl May & Jan Oberst & Gisela Lanza, 2024. "Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4259-4276, December.
    2. Walayat Hussain & Asma Musabah Alkalbani & Honghao Gao, 2021. "Forecasting with Machine Learning Techniques," Forecasting, MDPI, vol. 3(4), pages 1-2, November.

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