USING THE MINIMUM DISTANCE OF DMUs FROM THE FRONTIER OF THE PPS FOR EVALUATING GROUP PERFORMANCE OF DMUs IN DEA

Abstract

Evaluating group performance of decision-making units (DMUs) is an application of data envelopment analysis (DEA) and usually provides a measure to compare the frontiers of the production possibility sets (PPSs) corresponding to different groups and the internal inefficiencies of DMUs associated with their group. In this paper, first, a method is presented for obtaining the minimum distance of DMUs from the frontier of the PPS by ‖⋅‖1, which itself can be a very important subject in DEA, and then, for stating an application of these distances, an approach is provided for evaluating group performance of DMUs based on the production ability of the PPSs such that both constant and variable returns to scale assumptions can be used in this method in contrast with some other methods. Therefore, providing the methods for both obtaining the minimum distance of DMUs from the frontier of the PPS and evaluating group performance of DMUs is the most important contribution of this paper.

A modified distance friction minimization approach in data envelopment analysis

“…Later, Aparicio et al (2007) introduced a well-known single-stage procedure that solves a unique zero-one mixed-integer LP problem and Jahanshahloo et al (2012b) presented a linear bilevel programming problem for calculating the minimum distance of DMUs relative to the strongly efficient frontier. Jahanshahloo et al (2012a) provided two linear models based on l 1 and l ∞ norms to evaluate the group performance of DMUs relative to the efficient frontier which includes the weakly efficient boundary. Based on the least effort in obtaining the strong efficient projections, Aparicio et al (2017b, c) introduced Luenberger productivity change indicator.…”

Using Genetic Algorithms for Maximizing Technical Efficiency in Data Envelopment Analysis

“…Something similar happens with the other approaches devoted to maximizing technical efficiency. All of them are related in some sense to a big M or the direct calculation of all the efficient facets (see Jahanshahloo et al [11,12,13]). …”

Closest targets in environmental efficiency evaluation based on enhanced Russell measure

“…Portela et al (2004) determined the targets for the DMUs through a directional distance function approach. Jahanshahloo et al (2012) presented a method for obtaining the minimum distance of DMUs from the frontier of the PPS by |||| 1 . Briec and Lemaire (1999) , and Briec and Leleu (2003) used Hölder distance functions to obtain the evaluated DMU's minimum distance to the frontier.…”