IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-89869-4_5.html
   My bibliography  Save this book chapter

Stochastic Network Data Envelopment Analysis

In: Stochastic Benchmarking

Author

Listed:
  • Alireza Amirteimoori

    (Rasht Branch, Islamic Azad University)

  • Biresh K. Sahoo

    (XIM University)

  • Vincent Charles

    (Pontifical Catholic University of Peru)

  • Saber Mehdizadeh

    (Rasht Branch, Islamic Azad University)

Abstract

Most real-life production processes are multi-stage in nature. Characterization of such processes via concepts such as technical efficiency is considered important to firm managers for the stage-specific analysis of their business decisions in improving their performance. Therefore, it is imperative to estimate the efficiency of a firm not only for the network production system but also for its sub-processes to locate the sources of inefficiency. In this chapter, we deal with production processes characterized by a two-stage network structure that links their stage-specific processes with intermediate products (measures). In this two-stage production process, the first stage uses input resources to produce intermediate products, which are all, in turn, used as inputs in the second stage to produce final outputs.

Suggested Citation

  • Alireza Amirteimoori & Biresh K. Sahoo & Vincent Charles & Saber Mehdizadeh, 2022. "Stochastic Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Stochastic Benchmarking, chapter 0, pages 77-117, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-89869-4_5
    DOI: 10.1007/978-3-030-89869-4_5
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.

    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:isochp:978-3-030-89869-4_5. 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.