A procedure for large-scale DEA computations

Chen, Wen-Chih

doi:10.1016/j.cor.2008.05.006

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A procedure for large-scale DEA computations

2009-06-01

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Chen, Wen-Chih *

Cho, Wei-Jen

DOI：10.1016/j.cor.2008.05.006

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Abstract

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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. (C) 2008 Elsevier Ltd. All rights reserved.

Keywords:

Data envelopment analysis

Computational efficiency

Large-scale LP problems

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