IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v38y2021i05ns0217595921400030.html
   My bibliography  Save this article

A Semi-Online Algorithm for Single Machine Scheduling with Rejection

Author

Listed:
  • Sainan Guo

    (School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, P. R. China)

  • Ran Ma

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266525, P. R. China)

  • Yuzhong Zhang

    (Institute of Operations Research, School of Management, Qufu Normal University, Rizhao 276826, P. R. China)

  • Baoqiang Fan

    (Department of Mathematics and Information, Ludong University, Yantai 264025, P. R. China)

Abstract

In this paper, a single-machine semi-online scheduling problem with rejection is addressed. In this model, “semi-online” implies that pmax pmin ≤ γ, where pmax and pmin are the maximum processing time and the minimum one among all jobs, respectively, γ ≥ 1. In this setting, each job arrives online over time, and rejection is allowable. Our goal is minimizing the total penalty cost of rejected jobs plus the total completion time of processed jobs. The seminal result of this study is that we offer an algorithm with competitive ratio 1 + 1+γ(γ−1)−1 γ, which matches the result of the problem without rejection.

Suggested Citation

  • Sainan Guo & Ran Ma & Yuzhong Zhang & Baoqiang Fan, 2021. "A Semi-Online Algorithm for Single Machine Scheduling with Rejection," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(05), pages 1-21, October.
    • Handle: RePEc:wsi:apjorx:v:38:y:2021:i:05:n:s0217595921400030
    DOI: 10.1142/S0217595921400030
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217595921400030
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0217595921400030?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. Feifeng Zheng & Yuhong Chen & Ming Liu & Yinfeng Xu, 2022. "Competitive analysis of online machine rental and online parallel machine scheduling problems with workload fence," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1060-1076, September.

    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:wsi:apjorx:v:38:y:2021:i:05:n:s0217595921400030. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    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.