Assurance region (AR) restrictions on multipliers in data envelopment analysis (DEA) have been applied extensively in many performance measurement settings. They facilitate the derivation of multiplier values that reflect the reality of the problem situation under study. In measuring the operational efficiency of bank branches, for example, output multipliers would generally represent unit proc...

Abstract: In DEA, there are typically two schemes for measuring efficiency of DMUs; radial and non-radial. Radial models assume proportional change of inputs/outputs and usually remaining slacks are not directly accounted for inefficiency. On the other hand, non-radial models deal with slacks of each input/output individually and independently, and integrate them into an efficiency measure, cal...

Data Envelopment Analysis (DEA) is one of the best-known efficiency evaluation methods due to its advantages in selection of weights. Many research papers have extensively discussed the issue of weight restrictions, rather than those implied in the model itself. However, this often leads to a failure to represent the relations of certain weights, as well as underestimation of the efficiency of ...

Abstract Data Envelopment Analysis (DEA) can be regarded as a useful management tool to the assessment evaluation of decision making units (DMUs) using multiple inputs to produce multiple outputs. In some cases, to evaluate the efficiency having imprecise inputs and outputs such as fuzzy or interval data the efficiency of DMUs won’t be exact as well. Most researches have been conducted were bas...

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of a set of decision making units (DMUs), such as companies or public sector agencies. The main DEA models are only used for positive data. In recent years, some models have been presented to deal with negative data in DEA models. However, these models do not discriminate between efficient DMUs and only eval...

Data Envelopment Analysis is a linear programming technique for assessing the efficiency and productivity of decision making units (DMUs). Over the last decade, DEA has gained considerable attention as a managerial tool for measuring performance. The flexibility in selecting the weights in standard DEA models deters the comparison among DMUs on a common base. Moreover, these weights are unsuita...

In this paper, we extend the standard data envelopment analysis (DEA) model to include longer term top management goals. This extension is in recognition of the fact that benchmarking for decision making units (DMUs) is more than a purely monitoring process, and includes a component of future planning. The new model uses a goal programming structure to find points on the efficient frontier whic...

All the basic models in data envelopment analysis (DEA) divide decision making units (DMUs) in two groups: efficient DMUs and inefficient DMUs, and lack of discrimination of efficient units is a serious problem. Also in spite of completely ranking units in analytical hierarchy process (AHP), the process of making pairwise comparison matrix is based on experts’ choices and it causes error and in...

Data envelopment analysis (DEA) defines relative efficiency of decision making units (DMUs), using mathmatical programming. In evaluating relative efficiency, usually more than one unit may be efficient. The problem of ranking efficient DMUs is of interest from theoretical and practical point of view. In this paper different methods have been discussed and in some sense they are compared. Real ...

An important outcome of assessing relative efficiencies within a group of decision making units (DMUs) in fuzzy data envelopment analysis is a set of virtual multipliers or weights accorded to each (input or output) factor taken into account. These sets of weights are, typically, different for each of the participating DMUs, and in some cases it may be considered unacceptable that the same fact...