This paper investigates a procedure for identifying all efficient hyperplanes of production possibility set (PPS). This procedure is based on a method which recommended by Pekka J. Korhonen[8]. He offered using of lexicographic parametric programming method for recognizing all efficient units in data envelopment analysis (DEA). In this paper we can find efficient hyperplanes, via using the para...

The Production Possibility Set (PPS) is defined as the set of all inputs and outputs of a system in which inputs can produce outputs‎. ‎In Data Envelopment Analysis (DEA)‎, ‎it is highly important to identify the defining hyperplanes and especially the strong defining hyperplanes of the empirical PPS‎. ‎Although DEA models can determine the efficiency of a Decision Making Unit (DMU)‎, ‎but they...

window analysis is a known technique to evaluate performance during the time periods based on data envelopment analysis. in application of window analysis is assumed that efficient frontier have not changed during the periods in the study. unlike the window analysis, malmquist index tries to identify difference between efficient frontiers and separated them. but the means of different frontier ...

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 ...

One of the main important issues in Data Envelopment Analysis (DEA) is to recognize set anchor points which subset extreme efficient production possibility (PPS). An point an located on intersection strong frontier and weak frontier. In other word, each delineate from So, if a decision making unit (DMU) point, then there at least one supporting hyperplane whose gradient vector has some zero com...

This paper addresses issue of sensitivity of efficiency classification of variable returns to scale (VRS) technology for enhancing the credibility of data envelopment analysis (DEA) results in practical applications when an additional decision making unit (DMU) needs to be added to the set being considered. It also develops a structured approach to assisting practitioners in making an appropria...

Detecting the weak and strong congestion statuses of decision-making units (DMUs) measuring them via data envelopment analysis (DEA) is an important issue that has been discussed in several studies with different views. The efficiency frontier a concept derived from underlying production possibility set (PPS), related to them. Still, researchers have defined for each DMU many previous ignored l...

Network data envelopment analysis (NDEA) is an extension of standard DEA that models the efficiency assessment decision-making units (DMUs) by considering their internal structure. While in DEA, DMU regarded as a single process, NDEA, viewed network interconnected sub-processes (stages, divisions), where flow intermediate products (measures) essential assessment. In prevalent conventional metho...

Data Envelopment Analysis (DEA) is an appropriate tool for estimating various types of efficiency including Cost Efficiency (CE). Identifying and identifying inefficiency resources plays an important role in analyzing unit performance in order to plan for improving the status of Decision Making Units (DMUs). Now , if information on the inputs and outputs and pricing information of the evaluated...