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Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization

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  • Wu, Kan
  • McGinnis, Leon

Abstract

Performance evaluation plays a key role in manufacturing system design and productivity improvement. Characterizing performance objectively is the first step. Inspired by the underlying structure of tandem queues, we have derived an approximate model to characterize the system performance. The model decomposes system queue time and variability into bottleneck and non-bottleneck parts while capturing the dependence among workstations. Compared the new model with prior approaches, the new model not only is more accurate but also requires less information. The property of manufacturing system performance is given based on the insight from the model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:221:y:2012:i:2:p:328-339
    DOI: 10.1016/j.ejor.2012.03.019
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    References listed on IDEAS

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    Cited by:

    1. Vladimir Polotski & Jean-Pierre Kenne & Ali Gharbi, 2019. "Optimal production and corrective maintenance in a failure-prone manufacturing system under variable demand," Flexible Services and Manufacturing Journal, Springer, vol. 31(4), pages 894-925, December.
    2. Maddah, Bacel & Nasr, Walid W. & Charanek, Ali, 2017. "A multi-station system for reducing congestion in high-variability queues," European Journal of Operational Research, Elsevier, vol. 262(2), pages 602-619.
    3. Shi, Chuan & Gershwin, Stanley B., 2016. "Part sojourn time distribution in a two-machine line," European Journal of Operational Research, Elsevier, vol. 248(1), pages 146-158.
    4. 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.
    5. Stark, Oded & Budzinski, Wiktor & Kosiorowski, Grzegorz, 2019. "Switching queues, cultural conventions, and social welfare," European Journal of Operational Research, Elsevier, vol. 278(3), pages 837-844.
    6. Wu, Kan, 2014. "Taxonomy of batch queueing models in manufacturing systems," European Journal of Operational Research, Elsevier, vol. 237(1), pages 129-135.
    7. Morabito, Reinaldo & de Souza, Mauricio C. & Vazquez, Mariana, 2014. "Approximate decomposition methods for the analysis of multicommodity flow routing in generalized queuing networks," European Journal of Operational Research, Elsevier, vol. 232(3), pages 618-629.
    8. Kurz, Julian, 2016. "Capacity planning for a maintenance service provider with advanced information," European Journal of Operational Research, Elsevier, vol. 251(2), pages 466-477.
    9. Peter T. Vanberkel & Nelly Litvak & Martin L. Puterman & Scott Tyldesley, 2018. "Queuing network models for panel sizing in oncology," Queueing Systems: Theory and Applications, Springer, vol. 90(3), pages 291-306, December.
    10. Pourvaziri, Hani & Pierreval, Henri, 2017. "Dynamic facility layout problem based on open queuing network theory," European Journal of Operational Research, Elsevier, vol. 259(2), pages 538-553.
    11. Wu, Kan & Zhao, Ning, 2015. "Dependence among single stations in series and its applications in productivity improvement," European Journal of Operational Research, Elsevier, vol. 247(1), pages 245-258.

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