Data Envelopment Analysis and Big Data: Revisit with a Faster Method

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Khezrimotlagh et al. (Eur J Oper Res 274(3):1047–1054, 2019) propose a new framework to deal with large-scale data envelopment analysis (DEA). The framework provides the fastest available technique in the DEA literature to deal with big data. It is well known that as the number of decision-making units (DMUs) or the number of inputs–outputs increases, the size of DEA linear programming problems increases; and thus, the elapsed time to evaluate the performance of DMUs sharply increases. The framework selects a subsample of DMUs and identifies the set of all efficient DMUs. After that, users can apply DEA models with known efficient DMUs to evaluate the performance of inefficient DMUs or benchmark them. In this study, we elucidate their proposed method with transparent examples and illustrate how the framework is applied. Additional simulation exercises are designed to evaluate the performance of the framework in comparison with the performance of the two former methods: build hull (BH) and hierarchical decomposition (DH). The disadvantages of BH and HD are transparently demonstrated. A single computer with two different CPUs is used to run the methods. For the first time in the literature, we consider the cardinalities, 200,000, 500,000 and 1,000,000 DMUs.

Original languageEnglish (US)
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer
Pages1-34
Number of pages34
DOIs
StatePublished - 2020

Publication series

NameInternational Series in Operations Research and Management Science
Volume290
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

All Science Journal Classification (ASJC) codes

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

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