An introduction to data envelopment analysis

Alireza Amirteimoori*, Biresh K. Sahoo, Vincent Charles, Saber Mehdizadeh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Following the seminal work of Farrell (1957), Charnes et al. (1978) introduced DEA as a deterministic and nonparametric efficiency evaluation tool. DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units, DMUs (Charles et al., 2016, 2018; Tsolas et al., 2020). DEA is a data-oriented technique (Zhu, 2020) that is used to construct an empirical production frontier to measure efficiency. Note that the original DEA program of Charnes et al. (1978) is based on the CRS specification of technology and is used to measure the technical and scale efficiency of DMUs. However, Banker et al. (1984) extended this program to the case of VRS to estimate purely technical efficiency. Over the past three decades, DEA has been widely used to evaluate the relative efficiency of production firms, the nature of the returns-to-scale, and the productivity changes. The DEA literature has seen a wide variety of applications across a plethora of domains, having become a powerful management science tool (Charles et al., 2018). In this chapter, we briefly review the fundamental concepts in DEA, along with the basic technologies and programs.

Original languageEnglish
Title of host publicationStochastic benchmarking: theory and applications
EditorsAlizera Amirteimoori, Biresh K. Sahoo, Vincent Charles, Saber Mehdizadeh
PublisherSpringer Cham
Chapter2
Pages13-29
Number of pages17
ISBN (Electronic)9783030898694
ISBN (Print)9783030898687, 9783030898717
DOIs
Publication statusPublished - 11 Dec 2022
Externally publishedYes

Publication series

NameInternational Series in Operations Research and Management Science
Volume317
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Applied Mathematics

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