A procedure for large-scale DEA computations

Wen-Chih Chen*, Wei Jen Cho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Data envelopment analysis (DEA), a performance evaluation method, measures the relative efficiency of a particular decision making unit (DMU) against a peer group. Most popular DEA models can be solved using standard linear programming (LP) techniques and therefore, in theory, are considered as computationally easy. However, in practice, the computational load cannot be neglected for large-scale-in terms of number of DMUs-problems. This study proposes an accelerating procedure that properly identifies a few "similar" critical DMUs to compute DMU efficiency scores in a given set. Simulation results demonstrate that the proposed procedure is suitable for solving large-scale BCC problems when the percentage of efficient DMUs is high. The computational benefits of this procedure are significant especially when the number of inputs and outputs is small, which are most widely reported in the literature and practices.

Original languageEnglish
Pages (from-to)1813-1824
Number of pages12
JournalComputers and Operations Research
Volume36
Issue number6
DOIs
StatePublished - 1 Jun 2009

Keywords

  • Computational efficiency
  • Data envelopment analysis
  • Large-scale LP problems

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