Using super-efficiency, with regard to ranking efficient units, is increasing in DEA. However, this model has some problems such as the infeasibility. Thus, this article studies infeasibility of the input-based super-efficiency model (because of the zero inputs and outputs), and presents a solution by adding two virtual DMUs with mean values (one for inputs and one for outputs). Adding virtual ...

In this paper we investigate inputs/outputs estimate in the presence of undesirable factors. We discuss such questions: suppose some outputs (inputs) are undesirable and among a group of decision making units (DMUs) a DMU changes (increases and decreases simultaneously) some of its input (output) levels, how much outputs (inputs) could the unit change such that its efficiency index remains unch...

Data Envelopment Analysis (DEA) is a nonparametric approach for measuring the relative efficiency of a decision making units consists of multiple inputs and outputs. In all standard DEA models semi positive real valued measures are assumed, while in some real cases inputs and outputs may take complex valued. The question is related to measuring efficiency in such cases. As far as we are aware, ...

  In this article, Non radial method of dynamic DEA with the parallel network structure is presented and is used for calculation of relative efficiency measures when inputs and outputs do not change equally. In this model, DMU divisions under evaluation have been put together in parallel. But its dynamic structure is assumed in series. Since in real applications there are undesirable inputs an...

In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...

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...

Data envelopment analysis (DEA) is a technique based on linear programming (LP) to measure the relative efficiency of homogeneous units by considering inputs and outputs. The lack of discrimination among efficient decision making units (DMUs) and unrealistic input-outputs weights have been known as the drawback of DEA. In this paper the new scheme based on a goal programming data envelopment an...

We consider production technologies in which several parallel component processes are characterized by both component-specific and shared inputs outputs. The recently developed multicomponent variable constant returns-to-scale (MVRS MCRS) models of such based on the assumption that we have no information about actual allocation outputs to processes, is a common scenario many applications. MVRS ...

In some situations the producers desire to maximize total profit of Decision Making Units (DMUs) while the inputs and outputs prices of DMUs change from one time period to another. In this paper, the researchers develop productivity index when producers are going to maximize total profit when the price of inputs and outputs are known.The proposed method uses all price information about inputs a...