A modified distance friction minimization approach in data envelopment analysis

Abstract

A multi-step distance friction minimization (DFM) approach has been developed to assist a decision making unit to improve its efficiency. This approach contracts inputs and expands outputs simultaneously through the minimization of distance friction relative to the strongly efficient frontier based on a weighted Euclidean norm. In this paper, we point out that the DFM approach has a problem by means of two numerical examples and then show how to solve the problem. Using a real data set, we not only confirm the occurrence of this problem inherent in the original formulation, but also demonstrate how our modification works.

Coupling distinct MOLP interactive approaches with a novel DEA hybrid model

“…Non-radial measures such as the slacks based measure (SBM) proposed by Tone (2001), the enhanced Russell graph measure (ERGM) proposed in Pastor et al (1999) and the rangeadjusted measure developed by Cooper et al (1999) belong to this sort of models. In the second approach, the closest targets are computed for a given DMU according to a previously specified criterion of similarity, that is, considering the closeness between the values of the inputs and/or outputs of the DMU under assessment and the corresponding targets (see, e.g., Aparicio et al, 2007Aparicio et al, , 2017Fukuyama et al, 2014;Ruiz and Sirvent, 2020;Vakili et al, 2020). Although the least distance approach has the merit of potentially identifying the closest or easily acceptable efficient target for the DMs, we start by utilizing the greatest distance framework that has traditionally been employed because of its computational easiness (Fukuyama et al, 2014), then progressing towards the most preferred efficient target according to the DM's preferences.…”