On the identification of the global reference set in data envelopment analysis

TL;DR

This paper introduces a novel, LP-based method to efficiently identify the global reference set in non-radial DEA, accounting for multiple projections and supporting hyperplanes, with practical empirical validation.

Contribution

It defines the concepts of unary, maximal, and global reference sets in non-radial DEA and proposes a single LP approach for GRS identification, improving computational efficiency.

Findings

The LP approach effectively identifies the GRS in real data.

The method handles multiple projections and hyperplanes.

Empirical results demonstrate practical applicability.

Abstract

It is well established that multiple reference sets may occur for a decision making unit (DMU) in the non-radial DEA (data envelopment analysis) setting. As our first contribution, we differentiate between three types of reference set. First, we introduce the notion of unary reference set (URS) corresponding to a given projection of an evaluated DMU. The URS includes efficient DMUs that are active in a specific convex combination producing the projection. Because of the occurrence of multiple URSs, we introduce the notion of maximal reference set (MRS) and define it as the union of all the URSs associated with the given projection. Since multiple projections may occur in non-radial DEA models, we further define the union of the MRSs associated with all the projections as unique global reference set (GRS) of the evaluated DMU. As the second contribution, we propose and substantiate a general linear programming (LP) based approach to identify the GRS. Since our approach makes the identification through the execution of a single primal-based LP model, it is computationally more efficient than the existing methods for its easy implementation in practical applications. Our last contribution is to measure returns to scale using a non-radial DEA model. This method effectively deals with the occurrence of multiple supporting hyperplanes arising either from multiplicity of projections or from non-full dimensionality of minimum face. Finally, an empirical analysis is conducted based on a real-life data set to demonstrate the ready applicability of our approach.