A procedure for large-scale DEA computations

Wen-Chih Chen*, Wei Jen Cho

*Corresponding author for this work

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)1813-1824
期刊Computers and Operations Research
出版狀態Published - 1 六月 2009

指紋 深入研究「A procedure for large-scale DEA computations」主題。共同形成了獨特的指紋。