IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v26y1980i1p113-115.html
   My bibliography  Save this article

Note---The Single Facility Minimax Distance Problem Under Stochastic Location of Demand

Author

Listed:
  • Robert Carbone

    (Université Laval, Quebec)

  • Abraham Mehrez

    (University of Bengurion, Beersheba, Israel)

Abstract

This note extends the minimum maximum distance single facility location problem to situations where the locations of prospective demand points are considered to be random variables. Two types of decision are analyzed for this setting under the assumption of independent and identical normal distributions with the same means: locating on the basis of an expected value criterion or adopting a wait-and-see policy. Through the concept of the expected value of perfect information (EVPI), it is shown for one-dimensional location decisions that a substantial reduction in maximum distance may be realized by the adoption of a wait-and-see policy.

Suggested Citation

  • Robert Carbone & Abraham Mehrez, 1980. "Note---The Single Facility Minimax Distance Problem Under Stochastic Location of Demand," Management Science, INFORMS, vol. 26(1), pages 113-115, January.
  • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:1:p:113-115
    DOI: 10.1287/mnsc.26.1.113
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.26.1.113
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.26.1.113?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. Ling, Aifan & Sun, Jie & Xiu, Naihua & Yang, Xiaoguang, 2017. "Robust two-stage stochastic linear optimization with risk aversion," European Journal of Operational Research, Elsevier, vol. 256(1), pages 215-229.
    2. Dimitris Bertsimas & Xuan Vinh Doan & Karthik Natarajan & Chung-Piaw Teo, 2010. "Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion," Mathematics of Operations Research, INFORMS, vol. 35(3), pages 580-602, August.
    3. Mehrez, A. & Yuan, Y. & Gafni, A., 1995. "The search for information -- A patient perspective on multiple opinions," European Journal of Operational Research, Elsevier, vol. 85(2), pages 244-262, September.

    More about this item

    Keywords

    stochastic location; facility location;

    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:inm:ormnsc:v:26:y:1980:i:1:p:113-115. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.