IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/916978.html
   My bibliography  Save this article

Heuristic for Stochastic Online Flowshop Problem with Preemption Penalties

Author

Listed:
  • Mohammad Bayat
  • Mehdi Heydari
  • Mohammad Mahdavi Mazdeh

Abstract

The deterministic flowshop model is one of the most widely studied problems; whereas its stochastic equivalent has remained a challenge. Furthermore, the preemptive online stochastic flowshop problem has received much less attention, and most of the previous researches have considered a nonpreemptive version. Moreover, little attention has been devoted to the problems where a certain time penalty is incurred when preemption is allowed. This paper examines the preemptive stochastic online flowshop with the objective of minimizing the expected makespan. All the jobs arrive overtime, which means that the existence and the parameters of each job are unknown until its release date. The processing time of the jobs is stochastic and actual processing time is unknown until completion of the job. A heuristic procedure for this problem is presented, which is applicable whenever the job processing times are characterized by their means and standard deviation. The performance of the proposed heuristic method is explored using some numerical examples.

Suggested Citation

  • Mohammad Bayat & Mehdi Heydari & Mohammad Mahdavi Mazdeh, 2013. "Heuristic for Stochastic Online Flowshop Problem with Preemption Penalties," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-10, September.
    • Handle: RePEc:hin:jnddns:916978
    DOI: 10.1155/2013/916978
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2013/916978.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2013/916978.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/916978?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
    ---><---

    Citations

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


    Cited by:

    1. Xu, Changjin & Liu, Zixin & Pang, Yicheng & Akgül, Ali, 2023. "Stochastic analysis of a COVID-19 model with effects of vaccination and different transition rates: Real data approach," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

    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:hin:jnddns:916978. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.