IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4899-8068-7_1.html
   My bibliography  Save this book chapter

DEA for Two-Stage Networks: Efficiency Decompositions and Modeling Techniques

In: Data Envelopment Analysis

Author

Listed:
  • Wade D. Cook

    (York University)

  • Joe Zhu

    (Worcester Polytechnic Institute)

Abstract

Data envelopment analysis (DEA) is a method for identifying best practices among peer decision making units (DMUs). An important area of development in recent years has been that devoted to applications wherein DMUs represent network processes. One particular subset of such processes is those in which all the outputs from the first stage become inputs to the second stage. We call these types of DMU structures “two-stage networks”. Existing approaches in modeling efficiency of two-stage networks can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. There are two types of efficiency decomposition; multiplicative and additive. In multiplicative efficiency decomposition, the overall efficiency is defined as a product of the two individual stages’ efficiency scores, whereas in additive efficiency decomposition, the overall efficiency is defined as a weighted average of the two individual stages’ efficiency scores. We discuss modeling techniques used for solving two-stage network DEA models in linear programs.

Suggested Citation

  • Wade D. Cook & Joe Zhu, 2014. "DEA for Two-Stage Networks: Efficiency Decompositions and Modeling Techniques," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 1-29, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-8068-7_1
    DOI: 10.1007/978-1-4899-8068-7_1
    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. Patrizii, Vincenzo, 2020. "On network two stages variable returns to scale Dea models," Omega, Elsevier, vol. 97(C).
    2. Guccio, Calogero & Martorana, Marco & Mazza, Isidoro & Pignataro, Giacomo & Rizzo, Ilde, 2020. "An assessment of the performance of Italian public historical archives: Preservation vs utilisation," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1270-1286.

    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:978-1-4899-8068-7_1. 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.