IDEAS home Printed from https://ideas.repec.org/h/spr/oprchp/978-3-642-29210-1_72.html
   My bibliography  Save this book chapter

Enhancing Aggregate Production Planning with an Integrated Stochastic Queuing Model

In: Operations Research Proceedings 2011

Author

Listed:
  • Gerd J. Hahn

    (Catholic University of Eichstaett-Ingolstadt)

  • Chris Kaiser

    (Catholic University of Eichstaett-Ingolstadt)

  • Heinrich Kuhn

    (Catholic University of Eichstaett-Ingolstadt)

  • Lien Perdu

    (Catholic University of Leuven)

  • Nico J. Vandaele

    (Catholic University of Leuven)

Abstract

Mathematical models for Aggregate Production Planning (APP) typically omit the dynamics of the underlying production system due to variable workload levels since they assume fixed capacity buffers and predetermined lead times. Pertinent approaches to overcome these drawbacks are either restrictive in their modeling capabilities or prohibitive in their computational effort. In this paper, we introduce an Aggregate Stochastic Queuing (ASQ) model to anticipate capacity buffers and lead time offsets for each time bucket of the APP model. The ASQ model allows for flexible modeling of the underlying production system and the corresponding optimization algorithm is computationally very well tractable. The APP and the ASQ model are integrated into a hierarchical framework and are solved iteratively. A numerical example is used to highlight the benefits of this novel approach.

Suggested Citation

  • Gerd J. Hahn & Chris Kaiser & Heinrich Kuhn & Lien Perdu & Nico J. Vandaele, 2012. "Enhancing Aggregate Production Planning with an Integrated Stochastic Queuing Model," Operations Research Proceedings, in: Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), Operations Research Proceedings 2011, edition 127, pages 451-456, Springer.
    • Handle: RePEc:spr:oprchp:978-3-642-29210-1_72
    DOI: 10.1007/978-3-642-29210-1_72
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:oprchp:978-3-642-29210-1_72. 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: 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.