Title

An epsilon-based data envelopment analysis approach for solving performance measurement problems with interval and ordinal dual-role factors

Document Type

Article

Publication Date

12-9-2021

DOI

10.1007/s00291-021-00649-6

Abstract

Data envelopment analysis (DEA) is a linear programming method for measuring the performance and efficiency of units called decision-making units (DMUs). In many real-world performance measurement problems, the input and output data are not precisely known. Furthermore, the data may include dual-role factors that can be considered an input and output factor simultaneously. We propose a novel DEA model in the presence of imprecise data and imprecise dual-role factors by developing a new pair of mixed binary linear epsilon-based DEA models. The proposed models estimate the lower and upper bound efficiency scores in the presence of interval input, output, and dual-role factors by considering a fixed and unified production frontier for all DMUs. We then extend our models by including the weak ordinal dual-role factors. In contrast to the existing methods that exclude the dual-role factors, we include the dual-role factors and find a strictly positive value for the lower bound of the weights of inputs, outputs, and dual-role factors. We present a case study to demonstrate the applicability and exhibit the superiority of our approach over the existing methods.

Language

English

Comments

Ebrahimi, B., Tavana, M., Kleine, A. et al. An epsilon-based data envelopment analysis approach for solving performance measurement problems with interval and ordinal dual-role factors. OR Spectrum 43, 1103–1124 (2021).

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