A procedure for large-scale DEA computations

Wen-Chih Chen; Wei Jen Cho

doi:10.1016/j.cor.2008.05.006

A procedure for large-scale DEA computations

Wen-Chih Chen^*, Wei Jen Cho

^*Corresponding author for this work

Department of Industrial Engineering and Management

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	1813-1824
Number of pages	12
Journal	Computers and Operations Research
Volume	36
Issue number	6
DOIs	https://doi.org/10.1016/j.cor.2008.05.006
State	Published - 1 Jun 2009

Keywords

Computational efficiency
Data envelopment analysis
Large-scale LP problems

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AB - 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.

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KW - Large-scale LP problems

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A procedure for large-scale DEA computations

Abstract

Keywords

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