IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v31y2021i1p61-76id1554.html
   My bibliography  Save this article

Detecting congestion in DEA by solving one model

Author

Listed:
  • Maryam Shadab
  • Saber Saati
  • Reza Farzipoor Saen
  • Mohammad Khoveyni
  • Amin Mostafaee

Abstract

The presence of input congestion is one of the key issues that result in lower efficiency and performance in decision-making units (DMUs). So, determination of congestion is of prime importance, and removing it improves the performance of DMUs. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Since the output of inefficient units can be increased by keeping the input constant through projecting on the weak efficiency frontier, it is unnecessary to determine the congested inefficient DMUs. Therefore, in this case, we solely determine congested vertex units. Towards this aim, only one LP model in DEA is proposed and the status of congestion (strong congestion and weak congestion) obtained. In our method, a vertex unit under evaluation is eliminated from the production technology, and then, if there exists an activity that belongs to the production technology with lower inputs and higher outputs compared with the omitted unit, we say vertex unit evidences congestion. One of the features of our model is that by solving only one LP model and with easier and fewer calculations compared to other methods, congested units can be identified. Data set obtained from Japanese chain stores for a period of 27 years is used to demonstrate the applicability of the proposed model and the results are compared with some previous methods.

Suggested Citation

  • Maryam Shadab & Saber Saati & Reza Farzipoor Saen & Mohammad Khoveyni & Amin Mostafaee, 2021. "Detecting congestion in DEA by solving one model," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 61-76.
    • Handle: RePEc:wut:journl:v:31:y:2021:i:1:p:61-76:id:1554
    DOI: 10.37190/ord210105
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/1554%20-%20published.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord210105?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
    ---><---

    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:wut:journl:v:31:y:2021:i:1:p:61-76:id:1554. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.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.