Parallel Processing and Large-Scale Datasets in Data Envelopment Analysis

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


In order to measure the performance evaluation of a set of decision-making units (DMUs), a general data envelopment analysis (DEA) model should be solved once for each DMU. In data enabled analytics, when a large-scale dataset is evaluated, the elapsed time to apply a DEA model substantially increases. Parallel processing allows splitting the task into several parts so each part can simultaneously be executed on different processors. This study explores the impact of parallel processing to apply a DEA model for a large-scale dataset. The existing methods are clearly explained including their pros and cons. The methods are compared on different datasets according to three parameters: cardinality, dimension, and density. The strength of each existing method is changed when cardinality, dimension, density, and the number of processors in parallel are changed. A new methodology is proposed using the combination of two existing methods. In general, the proposed method is faster than all existing methods regardless of cardinalities, dimensions, and densities.

Original languageEnglish (US)
Title of host publicationInternational Series in Operations Research and Management Science
Number of pages16
StatePublished - 2021

Publication series

NameInternational Series in Operations Research and Management Science
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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