International Journal of Data Envelopment Analysis
Volume:1 Issue: 4, Autumn 2013

Data envelopment analysis (DEA) is a technique for measuring the efficiency of decision making units. In all models of the DEA, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. Given the possible large number of functional units, we use various ranking methods for evaluating units. One of the rating methods is Balance index and Topsis. This method has been used for categorical data. In this paper, we assume data as interval, introduce the interval Balance index and the interval Topsis and run it on a single example.

This research identifies returns to scale (RTS) of efficient decision making units (DMUs) with desirable (good) and undesirable (bad) inputs and outputs by presenting a new DEA (data envelopment analysis) approach. In this study, we first introduce a new input-output oriented model to determine efficient DMUs in the presence of undesirable factors and then, returns to scale of these DMUs are estimated by presenting a new non-radial DEA model. So far several RTS approaches has been proposed in DEA literature by many researchers, such as Banker and Thrall’s, Golany and Yu’s, Khodabakhshi’s et al., and Eslami and Khoveyni’s RTS approaches. In the proposed approaches, all inputs and outputs are respectively considered as desirable inputs and outputs while in real world, both desirable and undesirable data may be present. Note that advantage of our proposed approach is capable of estimating RTS of efficient DMUs in the presence of desirable and undesirable data. It is noticeable that, since an inefficient decision making unit (DMU) has more than one projection on the empirical function thus different returns to scales can be obtained for projections of the inefficient DMU by using our proposed RTS approach. Lastly, an empirical example for illustrating purpose is presented and also directions for future research are suggested.

Performance   evaluation in regular periods is one of the ways in which organizations can   evaluate their performance as well as weak and strong points unifiedly and   precisely. These days, mathematical models are also used to evaluate the   efficiency and productivity of various units. These units are a collection of   education, research and service activities as input and output factors and   according to effectiveness and importance degree of each factor in total   performance, the ratio of total weighed output to total weighed input are   calculated as efficiency degree of decision making units. In this study, data   envelopment analysis is employed to evaluate the efficiency of 48 sport   association board of Isfahan province based on championship perspective. In   present study, factors such as sending to matches, holding matches, the   number of players in national teams and etc. are used. Moreover, calculation   is done based on constant return on scale. Finding of efficiency calculation   reveal that out of 50 present boards in Isfahan province,   24 boards in men's group and 22 boards in women's group have been efficient   in year 90. After ranking blind and weak-sighted board, deaf board and   martial art board in women's group have been recognized in the first place.   Also, blind and weak-sighted board has the highest rank among 50 active   association board in men's group. Finally sensitivity analysis of input data   shows that sending to matches has the most significant effect on efficiency   of association boards.

A new algorithm for classification of DMUs to efficient and inefficient units in data envelopment analysis is presented. This algorithm uses the non- Archimedean Charnes- Cooper- Rhodes[1] (CCR) model. Also, it applies an assurance value for the non-Archimedean using only simple computations on inputs and outputs of DMUs (see [18]). The convergence and efficiency of the new  algorithm show the advantage of this algorithm compared to the Thrall’s algorithm (see [23]).

Inthis article we offer a method of ranking contractors by using DEA based onanalysis deficit and AHP. The process of hierarchical analysis (AHP) byproviding scales from paired comparison matrix, performs the contractor’sprioritizing choice. But AHP has some problems and to solve those problems,Jahanshahloo and his colleagues presented a new model which uses DEA andstandard deviation. In this article, AHP’s scales are calculated with theextension of DEA based on analysis deficit DEA-R (Ratio analysis). At the end,“Iranian Oil Pipeline and Telecommunication Company” contractors will be rankby the proposed method.

Data Envelopment Analysis (DEA) is an approach for evaluating performances of Decision Making Units (DMUs). The performances of DMUs are affected by the amount of sources that DMUs used. Usually increases in inputs cause increases in outputs. However, there are situations where increases in one or more inputs generate a reduction in one or more outputs. In such situations there is congestion in inputs or production process. In this study, we review two approaches that are available in the DEA literature for evaluating congestion. Afterwards, we focus on output losses due to congestion, and a model is introduced to compute output reduction. Then, the mentioned models are applied on an empirical example and the results are presented and interpreted.