—Data envelopment analysis (DEA) is a non- parametric method for relative efficiency evaluation of decision making units described by multiple inputs and multiple outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Two-stage or, in more general, multi-stage models with series or parallel structure (network models) belong among them. Standard DEA models are based on deterministic inputs and outputs. The paper deals with DEA network models under the assumption that inputs and/or outputs are continuous interval variables. Under this assumption the efficiency scores of decision making units are random variables as well. Several approaches for description of random efficiency scores were developed for standard DEA models but only few for models with network structure. They are mostly based on formulation of linear optimization problems. Another methodological approach for DEA models with interval data is simulation. The paper compares results given by simulation experiments and by optimization DEA network models with interval data.
—Data envelopment analysis, efficiency, interval data, two-stage model.
Josef Jablonsky is with the Department of Econometrics, University of Economics, Prague, 13067 Czech Republic (e-mail: jablon@ vse.cz).
Cite:Josef Jablonsky, "Two-Stage Data Envelopment Analysis Model with Interval Inputs and Outputs," International Journal of Trade, Economics and Finance vol.4, no.1, pp. 55-59, 2013.