Selecting slacks-based data envelopment analysis models

Mehdi Toloo, Kaoru Tone and Mohammad Izadikhah

European Journal of Operational Research, 2023, vol. 308, issue 3, 1302-1318

Abstract: Data envelopment analysis (DEA) is a well-known data-driven mathematical modeling approach that aims at evaluating the relative efficiency of a set of comparable decision making units (DMUs) with multiple inputs and multiple outputs. The number of inputs and outputs (performance factors) plays a vital role for successful applications of DEA. There is a statistical and empirical rule in DEA that if the number of performance factors is high in comparison with the number of DMUs, then a large percentage of the units will be determined as efficient, which is questionable and unacceptable in the performance evaluation context. However, in some real-world applications, the number of performance factors is relatively larger than the number of DMUs. To cope with this issue, selecting models have been developed to select a subset of performance factors that lead to acceptable results.

Keywords: Data envelopment analysis; slacks-based measure (SBM); Selecting models; Optimistic and pessimistic approaches; performance factors (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:308:y:2023:i:3:p:1302-1318

DOI: 10.1016/j.ejor.2022.12.032

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