IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v240y2021ics0925527321002188.html
   My bibliography  Save this article

Statistical analysis of family based dispatching rules and preemption

Author

Listed:
  • Jing, Hao
  • Sheng, Lijuan
  • Luo, Chaorui
  • Kwak, Choonjong

Abstract

Family based dispatching rules reduce time spent on setups to improve system performance. However, they can lead to bulky arrivals at subsequent stages, causing additional delays. Few studies have considered this effect so far. Preemption, on the other hand, can enforce the deadlines of important jobs in real situations. This study deals with a new production control problem of family based dispatching rules and preemption. No research has addressed both family based dispatching rules and preemption. A new framework of decision making is presented to solve this problem. The analysis of family based dispatching rules and preemption is carried out at a two-stage flow shop by simulation modeling and statistical analysis to discover several key factors. In addition, this research investigates the performance differences among different preemption types with respect to family based dispatching rules. The results of this paper can offer insight into the use of preemption, especially with family based dispatching rules.

Suggested Citation

  • Jing, Hao & Sheng, Lijuan & Luo, Chaorui & Kwak, Choonjong, 2021. "Statistical analysis of family based dispatching rules and preemption," International Journal of Production Economics, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:proeco:v:240:y:2021:i:c:s0925527321002188
    DOI: 10.1016/j.ijpe.2021.108242
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527321002188
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2021.108242?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durk-Jouke van der Zee, 2017. "Coordinating batching decisions in manufacturing networks," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5405-5422, September.
    2. Jonathan F. Bard & Shihui Jia & Rodolfo Chacon & John Stuber, 2015. "Integrating optimisation and simulation approaches for daily scheduling of assembly and test operations," International Journal of Production Research, Taylor & Francis Journals, vol. 53(9), pages 2617-2632, May.
    3. John S. Morris & Richard J. Tersine, 1990. "A Simulation Analysis of Factors Influencing the Attractiveness of Group Technology Cellular Layouts," Management Science, INFORMS, vol. 36(12), pages 1567-1578, December.
    4. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
    5. Yael Grushka-Cockayne & Victor Richmond R. Jose & Kenneth C. Lichtendahl Jr., 2017. "Ensembles of Overfit and Overconfident Forecasts," Management Science, INFORMS, vol. 63(4), pages 1110-1130, April.
    6. Mallor, Fermin & Guardiola, Ivan G., 2014. "The Weibull scheduling index for client driven manufacturing processes," International Journal of Production Economics, Elsevier, vol. 150(C), pages 225-238.
    7. Martin Paterok & Markus Ettl, 1994. "Sojourn Time and Waiting Time Distributions for M/GI/1 Queues with Preemption-Distance Priorities," Operations Research, INFORMS, vol. 42(6), pages 1146-1161, December.
    8. Andres, Carlos & Albarracin, Jose Miguel & Tormo, Guillermina & Vicens, Eduardo & Garcia-Sabater, Jose Pedro, 2005. "Group technology in a hybrid flowshop environment: A case study," European Journal of Operational Research, Elsevier, vol. 167(1), pages 272-281, November.
    9. Kenneth R. Baker, 1984. "Sequencing Rules and Due-Date Assignments in a Job Shop," Management Science, INFORMS, vol. 30(9), pages 1093-1104, September.
    10. Nomden, Gert & van der Zee, Durk-Jouke, 2008. "Virtual cellular manufacturing: Configuring routing flexibility," International Journal of Production Economics, Elsevier, vol. 112(1), pages 439-451, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kannan, V. R. & Ghosh, S., 1995. "Using dynamic cellular manufacturing to simplify scheduling in cell based production systems," Omega, Elsevier, vol. 23(4), pages 443-452, August.
    2. Yannik Zeiträg & José Rui Figueira, 2023. "Automatically evolving preference-based dispatching rules for multi-objective job shop scheduling," Journal of Scheduling, Springer, vol. 26(3), pages 289-314, June.
    3. Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    4. A. Baykal Hafızoğlu & Esma S. Gel & Pınar Keskinocak, 2013. "Expected Tardiness Computations in Multiclass Priority M / M / c Queues," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 364-376, May.
    5. Sarper, H. & Henry, M. C., 1996. "Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop," Omega, Elsevier, vol. 24(1), pages 73-81, February.
    6. van Ooijen, H. P. G. & Bertrand, J. W. M., 2001. "Economic due-date setting in job-shops based on routing and workload dependent flow time distribution functions," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 261-268, December.
    7. Vikrant Sharma & Sundeep Kumar & M. L. Meena, 2022. "Key criteria influencing cellular manufacturing system: a fuzzy AHP model," Journal of Business Economics, Springer, vol. 92(1), pages 65-84, January.
    8. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    9. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    10. Hassan, Mohsen M. D., 1995. "Layout design in group technology manufacturing," International Journal of Production Economics, Elsevier, vol. 38(2-3), pages 173-188, March.
    11. Tidarat Luangrungruang & Urachart Kokaew, 2022. "Adapting Fleming-Type Learning Style Classifications to Deaf Student Behavior," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    12. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    13. Baykasoglu, Adil & ÖzbakIr, Lale, 2010. "Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system," International Journal of Production Economics, Elsevier, vol. 124(2), pages 369-381, April.
    14. Thürer, Matthias & Stevenson, Mark & Silva, Cristovao & Qu, Ting, 2017. "Drum-buffer-rope and workload control in High-variety flow and job shops with bottlenecks: An assessment by simulation," International Journal of Production Economics, Elsevier, vol. 188(C), pages 116-127.
    15. Adenso-Diaz, Belarmino, 1996. "An SA/TS mixture algorithm for the scheduling tardiness problem," European Journal of Operational Research, Elsevier, vol. 88(3), pages 516-524, February.
    16. Steve Derkic & James E. Stafford, 2002. "Symbolic Computation of Moments in Priority Queues," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 261-277, August.
    17. Sabuncuoglu, I. & Bayiz, M., 1999. "Job shop scheduling with beam search," European Journal of Operational Research, Elsevier, vol. 118(2), pages 390-412, October.
    18. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    19. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    20. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:240:y:2021:i:c:s0925527321002188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.