IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v71y2020i6p910-927.html
   My bibliography  Save this article

Minimizing the total weighted tardiness of overlapping jobs on parallel machines with a learning effect

Author

Listed:
  • Jen-Ya Wang

Abstract

The influence of learning effects on job scheduling has been studied for years. By considering learning effects, an operator can schedule jobs in a more precise way and improve on the original schedules. In fact, jobs with duplicate contents (i.e., overlapping jobs) do not require as much processing time as do disjoint jobs. However, this phenomenon is seldom discussed in traditional scheduling models. In this study, a parallel-machine scheduling problem with a learning effect and an overlap effect is introduced. The objective is to minimise the total weighted tardiness of jobs whose processing times are influenced by both effects. A branch-and-bound algorithm comprising a lower bound algorithm is developed to generate the optimal schedules. Compared with past research, two main contributions are made. First, a model considering simultaneously both the learning effect and the overlap effect is proposed. Second, an efficient lower bound algorithm accelerating the execution speed is developed. At the end, computational experiments are conducted to show the execution efficiency and cost effectiveness.

Suggested Citation

  • Jen-Ya Wang, 2020. "Minimizing the total weighted tardiness of overlapping jobs on parallel machines with a learning effect," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(6), pages 910-927, June.
    • Handle: RePEc:taf:tjorxx:v:71:y:2020:i:6:p:910-927
    DOI: 10.1080/01605682.2019.1590511
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2019.1590511
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01605682.2019.1590511?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.

    Citations

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


    Cited by:

    1. Chung-Ho Su & Jen-Ya Wang, 2022. "A Branch-and-Bound Algorithm for Minimizing the Total Tardiness of Multiple Developers," Mathematics, MDPI, vol. 10(7), pages 1-24, April.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tjorxx:v:71:y:2020:i:6:p:910-927. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.