IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i5p1621-1632.html
   My bibliography  Save this article

G-network models to support planning for disaster relief distribution

Author

Listed:
  • Merve Ozen
  • Ananth Krishnamurthy

Abstract

One of the key activities during disaster response is distributing relief items to victims. This is a challenging task due to dynamically changing victim needs and disaster aftermath conditions. We model the distribution operations where items like tarpaulins and blankets are distributed by volunteers, to victims at temporary distribution areas called relief centers (RC). We investigate the impact victim movements have on the distribution performance. We model each RC as a queue, and the distribution operation as a generalised queuing network (G-network). We investigate product form solutions for the proposed G-network model, and prove a new product form result for G-networks with signals and batch transfer under certain conditions. We leverage this result to develop product form approximations that apply across a broad range of settings. We apply the G-network model to a case study using the Nepal earthquake relief distribution data, and quantify the impact of victim movement on network performance.

Suggested Citation

  • Merve Ozen & Ananth Krishnamurthy, 2022. "G-network models to support planning for disaster relief distribution," International Journal of Production Research, Taylor & Francis Journals, vol. 60(5), pages 1621-1632, March.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:5:p:1621-1632
    DOI: 10.1080/00207543.2020.1867920
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2020.1867920
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2020.1867920?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.

    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:tprsxx:v:60:y:2022:i:5:p:1621-1632. 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/TPRS20 .

    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.