IDEAS home Printed from https://ideas.repec.org/a/hin/complx/9198737.html
   My bibliography  Save this article

Performance Analysis in Production Systems with Uncertain Data: A Stochastic Data Envelopment Analysis Approach

Author

Listed:
  • Seyedeh Fatemeh Bagheri
  • Alireza Amirteimoori
  • Sohrab Kordrostami
  • Mansour Soufi
  • Zeljko Stevic

Abstract

The problem of determining an optimal benchmark to inefficient decision-making units (DMUs) is an important issue in the field of performance analysis. Previous methods for determining the projection points of inefficient DMUs have only focused on one objective and other features have been ignored. This paper attempts to determine the best projection point for each DMU when the inputs and outputs data are in stochastic form and presents an alternative definition for the best projection by considering three main aspects: technical efficient, minimal cost, and maximal revenue as much as possible. Considering the important role of the electricity industry in the economic growth of each country, a practical example has been implemented on 16 regional electricity companies in Iran in 9 consecutive periods. The efficiency score along with the projection points of the three technical models (BCC model of Banker et al. (1984)), cost, and stochastic revenue are compared with the projection point obtained from the model presented in this article, which simultaneously meets these three objectives, showing the improvement of companies’ performance.

Suggested Citation

  • Seyedeh Fatemeh Bagheri & Alireza Amirteimoori & Sohrab Kordrostami & Mansour Soufi & Zeljko Stevic, 2022. "Performance Analysis in Production Systems with Uncertain Data: A Stochastic Data Envelopment Analysis Approach," Complexity, Hindawi, vol. 2022, pages 1-14, October.
    • Handle: RePEc:hin:complx:9198737
    DOI: 10.1155/2022/9198737
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2022/9198737.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2022/9198737.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9198737?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
    ---><---

    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:complx:9198737. 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.