International Journal of Data Envelopment Analysis
Volume:2 Issue: 4, Autumn 2014

The calculation of RTS amounts to measuring a relationship between inputs and outputs in a production structure. There are many methods to measure RTS in the primal space or the dual space. One of the main approaches is using the multiplier on the convexity constraint. But returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In this paper discusses methods for determining returns to scale in the presence of undesirable (bad) outputs in the regulated environments.

Data Envelopment Analysis (DEA) as a non-parametric method for efficiency measurement allows decision making units (DMUs) to select the most advantageous weight factors in order to maximize their efficiency scores.  In most practical applications of DEA presented in the literature, the presented models assume that all inputs are fully desirable. However, in many real situations undesirable inputs are part of the production process. In order to deal with undesirable inputs, this paper changes the undesirable inputs to be desirable ones by reversing, then a compromise solution approach is proposed to generate a common set of weights under DEA framework. The DEA efficiencies obtained with the most favorable weights to each DMU are treated as the target efficiencies of DMUs. Based on the generalized measure of distance, three types of DEA-based efficiency score programming can be derived. The proposed approach is then applied to real-world data set that characterize the performance of seven types of chemical activities.

Data Envelopment Analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) with multiple inputs and outputs. The traditional DEA treats decision making units under evaluation as black boxes and calculates their efficiencies with first inputs and last outputs. This carries the notion of missing some intermediate measures in the process of changing the inputs to outputs of DMU and as a result the effect of these measures in the process of performance evaluation is not considered. Recently, some models are created in DEA which can evaluate the system in multi stages and consider the relations between the systems. The objective of this paper is to investigate efficiency decomposition in a three-stage process that has a two independent parallel stages linking with a one final stage. This three stage processes calculate the efficiency of organization with considering intermediate constraints. Finally, we illustrate the proposed method with numerical example

Negative data handling has gained a remarkable importance in the literature of Data Envelopment Analysis (DEA) to address many real life problems. Various erstwhile applications, in this arena, referred relocation of the origin to a superior (RDM) or to an inferior (Translated Input Oriented BCC) neighboring point. In this paper, the conditions for Rotation Invariance of various Data Envelopment Analysis models are discussed. Specifically, in presence of partially negative data, a rotation using the Cone Ratio model, beyond a threshold value of the oblique index does not alter the efficient frontier. So, a solution can be obtained without relocating the origin. In this context, two models, termed as Input Oriented BCC model with Relocated Origin  (IOBCC-RO) and Input oriented BCC model with Rotated Axis (IOBCC-RA), are applied on a case of "the notional effluent processing system" (from Sharp et al (2006)) to observe their impact on the radial efficiency scores.

This paper proposes an alternative approach for efficiency analysis when a set of DMUs uses interval scale variables in the productive process. To test the influence of these variables, we present a general approach of deriving DEA models to deal with the variables. We investigate a number of performance measures with unrestricted-in-sign interval and/or interval scale variables.

Recently, Farzipoor Saen [Journal of the Operational Research Society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. This short communication aims at showing a computational error in computing the value of preference intensity parameter in Farzipoor Saen’s [1] article. Then, a ranking method is used to identify the suppliers with the best performance.