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Operation-based flowtime estimation in a dynamic job shop

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  • Sabuncuoglu, I.
  • Comlekci, A.

Abstract

In the scheduling literature, estimation of job flowtimes has been an important issue since the late 1960s. The previous studies focus on the problem in the context of due date assignment and develop methods using aggregate information in the estimation process. In this study, we propose a new flowtime estimation method that utilizes the detailed job, shop and route information for operations of jobs as well as the machine imbalance information. This type of information is now available in computer-integrated manufacturing systems. The performance of the proposed method is measured by computer simulation under various experimental conditions. It is compared with the existing flowtime estimation methods for a wide variety of performance measures. The results indicate that the proposed method outperforms all the other flowtime estimation methods. Moreover, it is quite robust to changing shop conditions (i.e., machine breakdowns, arrival rate and processing time variations, etc.). A comprehensive bibliography is also provided in the paper.

Suggested Citation

  • Sabuncuoglu, I. & Comlekci, A., 2002. "Operation-based flowtime estimation in a dynamic job shop," Omega, Elsevier, vol. 30(6), pages 423-442, December.
    • Handle: RePEc:eee:jomega:v:30:y:2002:i:6:p:423-442
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    References listed on IDEAS

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    1. Cheng, T. C. E., 1986. "Due-date determination for an single-machine shop with SPT dispatching," Engineering Costs and Production Economics, Elsevier, vol. 10(1), pages 35-41, March.
    2. Enns, S. T., 1998. "Lead time selection and the behaviour of work flow in job shops," European Journal of Operational Research, Elsevier, vol. 109(1), pages 122-136, August.
    3. James K. Weeks, 1979. "A Simulation Study of Predictable Due-Dates," Management Science, INFORMS, vol. 25(4), pages 363-373, April.
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    5. Enns, S. T., 1994. "Job shop leadtime requirements under conditions of controlled delivery performance," European Journal of Operational Research, Elsevier, vol. 77(3), pages 429-439, September.
    6. Ramasesh, R, 1990. "Dynamic job shop scheduling: A survey of simulation research," Omega, Elsevier, vol. 18(1), pages 43-57.
    7. J. W. M. Bertrand, 1983. "The Effect of Workload Dependent Due-Dates on Job Shop Performance," Management Science, INFORMS, vol. 29(7), pages 799-816, July.
    8. Cheng, T. C. E. & Gupta, M. C., 1989. "Survey of scheduling research involving due date determination decisions," European Journal of Operational Research, Elsevier, vol. 38(2), pages 156-166, January.
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    Cited by:

    1. Kasper, T.A. Arno & Land, Martin J. & Teunter, Ruud H., 2023. "Non-hierarchical work-in-progress control in manufacturing," International Journal of Production Economics, Elsevier, vol. 257(C).
    2. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
    3. Chuang Wang & Pingyu Jiang, 2019. "Deep neural networks based order completion time prediction by using real-time job shop RFID data," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1303-1318, March.
    4. Baykasoglu, Adil & Gocken, Mustafa & Unutmaz, Zeynep D., 2008. "New approaches to due date assignment in job shops," European Journal of Operational Research, Elsevier, vol. 187(1), pages 31-45, May.
    5. Kasper, T.A. Arno & Land, Martin J. & Teunter, Ruud H., 2023. "Towards System State Dispatching in High‐Variety Manufacturing," Omega, Elsevier, vol. 114(C).
    6. Ozturk, Atakan & Kayaligil, Sinan & Ozdemirel, Nur E., 2006. "Manufacturing lead time estimation using data mining," European Journal of Operational Research, Elsevier, vol. 173(2), pages 683-700, September.

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