Abstract
In this paper, we present a comparison of robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the data envelopment analysis (DEA) model is modeled with a probability distribution, through the traditional cross-efficiency evaluation procedure. We evaluate the ranking obtained from these scores and analyze the robustness of these rankings, in such a way that any changes respect the set of units selected for the analysis. The probabilistic approach allows us to obtain two different robust efficiency scores: the unconditional expected score and the expected score under the assumption of maximum entropy principle. The calculation of these efficiency scores involves the resolution of an exponential number of linear problems. We also present an algorithm to estimate the robust scores in an affordable computational time.
The authors thank the financial support from the Spanish Ministry for Economy and Competitiveness (Ministerio de Economía, Industria y Competitividad), the State Research Agency (Agencia Estatal de Investigación), and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional) under grant MTM2016-79765-P (AEI/FEDER, UE).
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Aparicio, J., Monge, J.F. (2020). Robust DEA Efficiency Scores: A Heuristic for the Combinatorial/Probabilistic Approach. In: Aparicio, J., Lovell, C., Pastor, J., Zhu, J. (eds) Advances in Efficiency and Productivity II. International Series in Operations Research & Management Science, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-41618-8_8
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