Robust DEA efficiency scores: A probabilistic/combinatorial approach

被引:22
|
作者
Landete, Mercedes [1 ]
Monge, Juan F. [1 ]
Ruiz, Jose L. [1 ]
机构
[1] Univ Miguel Hernandez, Ctr Invest Operat, Avda Univ S-N, Elche 03202, Alicante, Spain
关键词
Data envelopment analysis; Model specification; Efficiency measurement; Robustness; DATA ENVELOPMENT ANALYSIS; VARIABLE SELECTION TECHNIQUES; MODELS; INDICATOR; TESTS; UNITS;
D O I
10.1016/j.eswa.2017.05.072
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This probabilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:145 / 154
页数:10
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