IDEAS home Printed from https://ideas.repec.org/a/spr/jsched/v23y2020i3d10.1007_s10951-019-00627-5.html
   My bibliography  Save this article

Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant

Author

Listed:
  • Zachariah Stevenson

    (University of Waterloo)

  • Ricardo Fukasawa

    (University of Waterloo)

  • Luis Ricardez-Sandoval

    (University of Waterloo)

Abstract

Periodic rescheduling is a commonly used method for scheduling short-term operations. Through computational experiments that vary plant parameters, such as the load and the capacity of a facility, we investigate the effects these parameters have on plant performance under periodic rescheduling. The results show that choosing a suitable rescheduling policy depends highly on some key plant parameters. In particular, by modifying various parameters of the facility, the performance ranking of the various rescheduling policies may be reversed compared to the results obtained with nominal parameter values. This highlights the need to consider both facility characteristics and what the crucial objective of the facility is when selecting a rescheduling policy. This study considers a variant of the job shop problem, used to model the operation of an industrial-scale analytical services facility using different periodic rescheduling policies. A rolling horizon routine is used to schedule operations over the scheduling horizon. Performance is measured in terms of job throughput, job makespan, and proportion of jobs on time at the end of the scheduling horizon to obtain a more complete understanding of how performance varies between rescheduling policies.

Suggested Citation

  • Zachariah Stevenson & Ricardo Fukasawa & Luis Ricardez-Sandoval, 2020. "Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant," Journal of Scheduling, Springer, vol. 23(3), pages 397-410, June.
  • Handle: RePEc:spr:jsched:v:23:y:2020:i:3:d:10.1007_s10951-019-00627-5
    DOI: 10.1007/s10951-019-00627-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10951-019-00627-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10951-019-00627-5?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. Michele E. Pfund & John W. Fowler, 2017. "Extending the boundaries between scheduling and dispatching: hedging and rescheduling techniques," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3294-3307, June.
    2. Adil Baykasoğlu & Fatma S. Karaslan, 2017. "Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3308-3325, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Didden, Jeroen B.H.C. & Dang, Quang-Vinh & Adan, Ivo J.B.F., 2024. "Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0," European Journal of Operational Research, Elsevier, vol. 316(2), pages 569-583.

    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. Yu Pu & Fang Li & Shahin Rahimifard, 2024. "Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments," Sustainability, MDPI, vol. 16(8), pages 1-26, April.
    2. Jinfeng Liu & Qiukai Ji & Xiaohu Zhang & Yu Chen & Yiming Zhang & Xiaojun Liu & Mingming Tang, 2024. "Digital twin model-driven capacity evaluation and scheduling optimization for ship welding production line," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3353-3375, October.
    3. Ali Fırat İnal & Çağrı Sel & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2023. "A Multi-Agent Reinforcement Learning Approach to the Dynamic Job Shop Scheduling Problem," Sustainability, MDPI, vol. 15(10), pages 1-24, May.

    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:spr:jsched:v:23:y:2020:i:3:d:10.1007_s10951-019-00627-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.