INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS
Year:2015 | Volume:3 | Issue:3 (11)|Papers Count:6

Data envelope analysis (DEA) is an approach to estimate the relative efficiency of decision making units (DMUs). Several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (CEM) were presented. Besides, a number of DEA models with interval data have been developed and ranking DMUs with such data was solved. However, presenting an obtained crisp data derived interval data is a critical problem, so that many researches were implemented so as to compute weights and averaging the interval data. In this paper we propose the new algorithm to find more suitable weight applying a data mining approach of DMU’s data. For this purpose, we employed clustering and pair-wise comparison matrix on given relative efficiency from CEM. Results indicate there is meaningful different between efficiency of DMUs with lower bound and that of DMUs with upper bound.

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appear. However, with the widespread use of interval scale data, the emphasis has been directed towards the simultaneous consideration of the ratio and interval scale data in DEA models. This study, introduces a measure of inefficiency and identifies efficient units as is done in DEA models with VRS technology based on separating hyper planes. The basic idea in the approach is to obtain a separating hyperplane of DMUs so that the hyper plane can separate the maximum number of DMUs whose performances are not better than a DMU under evaluation, from the rest of the DMUs. Performance measure is defined as a ratio of not-better units to all units. Also, this paper presents a relationship between the performance measures with those in DEA models with VRS technology.

Examining the impacts of data modification is considered as sensitivity analysis. A lot of studies have considered the data modification of inputs and outputs in DEA. The issues which has not heretofore been considered in DEA sensitivity analysis is modification in the number of inputs and (or) outputs and determining the impacts of this modification in the status of efficiency of DMUs. This paper  is going to present systems that show the impacts of adding one or multiple inputs or outputs on the status of efficiency of DMUs and furthermore a model is presented for recognizing the minimum number of inputs and (or) outputs from among specified inputs and outputs which can be added whereas an inefficient DMU will become efficient. Finally the presented systems and model have been utilized for a set of real data and the results have been reported.

Data Envelopment Analysis (DEA) is a technique that uses all collected observations to measure performance. This method presents no data about how to operate on DMU. The present research attempted to study a unit with all its subunits, if the unit is efficient, it means that all its subunits are efficient too and if it is an inefficient, it shows clearly that which one of the subunits makes inefficiency in order to reach to desired performance by correcting just that submit. Studying the performance by each of these DEA models is time-consuming and long. By using general DEA model (GDEA) we can reach to better speed in evaluation of working with five mentioned DEA methods. The present research attempted to study the unit's performance of general GDEA model in presence of subunits, and a general model illustrated to evaluation of unit performance in presence of decision-making subunits.

In this research, efficiency of Iranian forest and rangeland decision making unit was assessed using non-parametric method. Inputs and outputs determined by field research and with applying a Delphi method the final input and output variables selected. Forty five wood-producing units (forestry plans) in three northern Iranian province were studied using Data Envelopment Analysis. The efficiency of wood-producing units were calculated in two forms; constant returns to scale (technical efficiency) and variable returns to scale (scale efficiency). The results indicated that 5 units out of 45 units were fully efficient by reaching to the technical efficiency of 40.38% and9 units reached to the scale efficiency of 55.47%. The result concluded that about 89% of wood producing units were not efficient technically and 80% by scale, which required forest and rangeland decision making authority to enhance inputs in the same level of outputs by suitable policies and strategies.

In this paper, using the Value Efficiency in DEA pattern by taking decisive obtain. Generally, non-radial model of VE and use proper weight to reduce input and output gain are the appropriate pattern. This paper introduces a multi-objective linear programming problem and efficiency determines the value of inefficient units. Use interactive methods to solve MOLP proposed and applied. In conclusion, to study the scope of human resources Parsian gas refinery runs and the pattern of the proposed models It has been found inefficient units.