Negative data in DEA: a simple proportional distance function approach

Abstract

The need to adapt Data Envelopment Analysis (DEA) and other frontier models in the context of negative data has been a rather neglected issue in the literature. Silva Portela, Thanassoulis, and Simpson ( 2004) proposed a variation on the directional distance function, a very general distance function that is dual to the profit function, to accommodate eventual negative data. In this contribution, we suggest a simple variation on the proportional distance function that can do the same job.

Translation Invariance in Data Envelopment Analysis

“…However, a new approach has recently been published that criticizes the RDM with respect to its interpretation. Specifically, Kerstens and Van de Woestyne (2011) argue that a straightforward modification of the well-known proportional distance function may equally be used to accommodate negative data, with the advantage of having a simpler interpretation in terms of the percentage change that facilitates its use in a managerial context. The model that Kerstens and Van de Woestyne (2011) propose to solve is the following:…”

“…Kordrostami and Noveiri (2012) extended the SORM model so as to accommodate what they call flexible variables, i.e., variables that can be used at the same time as an input and as an output. Finally, Cheng et al (2013) propose a variant of the traditional radial models, called VRM, which are nothing other than particular oriented cases of the model published two years earlier by Kerstens and Van de Woestyne (2011).…”

A directional distance based super-efficiency DEA model handling negative data

“…Emrouznejad et al (2010) propose a semi-oriented radial measure (SORM) to handle negative input-output data. Kerstens and Woestyne (2011) recommend a generalized Farrell proportional distance function that handles negative data and maintains a proportional interpretation under mild conditions. Cheng et al (2013) find that the SORM model might lead to worse target inputs or outputs than the original ones for inefficient DMUs.…”

A state-of-art review on supplier selection problem

“…Investment 4 Lozano & Villa, 2005Park, 2007;Wong & Wong, 2007;Bao et al, 2008;Soleimani-damaneh, 2009;Alem et al, 2009;Dotoli et al, 2010;Barnum et al, 2010. Business 8 Saranga & Moser, 2010;Chen, 2010;. Marketing 3 Kang, 2010Samoilenko & Osei-Bryson, 2010;Milioni et al, 2011;Chen & Delmas, 2011;Kerstens & Woestyne, 2011;You & Yan, 2011;Zhou et al, 2011;Liu & Lu, 2012;Wang et al, 2012;Guedes et al, 2012;Akçay, 2012;Samoilenko, 2012;Samoilenko, 2012;Amindoust et al, 2012;Davoodi et al, 2012;Huang & Kao, 2012;Huang & Kao, 2012;Puri & Yadav, 2013; Organization 18 Braglia & Petroni, 2000;Triantis, 2004;Greasley, 2005;Sevkli et al, 2007;Chuang et al, 2009;Feng & Jiang, 2009;Xu et al, 2009;Bandyopadhyay, 2011. Product 8 Liu andWu, 2005;Seydel, 2006;Talluria et al, 2006;Ramanathan, 2007;Çelebi & Bayraktar, 2008;Zhao ...…”