As a non-parametric method of relative efficient measurement of a group of decision-making units (〖DMU〗_s), Data Envelopment Analysis (DEA) is one of the most important tools in efficiency computation. One of the main concerns dealt with in DEA is dealing with return to scale in two-stage processes in which, produced outputs of the first stage inputs are used as inputs for the second stage. The...

The non-cooperative two-stage network DEA approach requires the assumption as to whether the first or second stage is the leader or follower. In many real-world situations such as evaluating sustainable design performances, however, information about the leader (dominant) stage is often unavailable as a firm ׳s internal decision process may not be observable for outsiders. In the current study,...

It is essential for most organizations and financial institutes to be able to evaluate their decision-making units (DMUs), when there is only a ratio of inputs to outputs (or vice versa) available. In this paper, we will propose our two-stage DEA-R models, which are a combination of data envelopment analysis and ratio data, based on value efficiency. Integrating value efficiency into data envel...

Agriculture provides commodities and has an influence on the environment. However, even if the value of agricultural production is easy to evaluate, it is difficult to estimate the efficiency of agricultural production at site level. Furthermore, the valuation of environmental impact is complex. Non-parametric approaches such as DEA allow for an estimation of environment and economic performanc...

Our motivation is to detail a potential improvement on the three-stage analysis published by Fried et al. [Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 2002;17:157–74] that can distinguish true performers from those that may be advantaged by favourable environments or measurement errors. The method starts with data env...

Data envelopment analysis (DEA) is a non-parametric technique for evaluation of relative efficiency of decision making units described by multiple inputs and outputs. It is based on solving linear programming problems. Since 1978 when basic DEA model was introduced many its modifications were formulated. Among them are two or multi-stage models with serial or parallel structure often called net...

Dynamic network data envelopment analysis (DNDEA) has attracted a lot of attention in recent years. On one hand the available models in DNDEA evaluating the performance of a DMU with interrelated processes during specified multiple periods but on the other hand they can only measure the efficiency of dynamic network structure when a supply chain structure present. For example, in the banking in...

The units found strongly efficient in DEA studies on efficiency can be divided into self-evaluators and active peers, depending on whether the peers are referencing any inefficient units or not. The contribution of the paper starts with subdividing the selfevaluators into interior and exterior ones. The exterior self-evaluators are efficient “by default”; there is no firm evidence from observat...