INTERNATIONAL JOURNAL OF DATA ENVELOPMENT ANALYSIS
Year:2016 | Volume:4 | Issue:3 (15)|Papers Count:11

In data envelopment analysis (DEA), several secondary goals have already been proposed in order to overcome the problem of the DEA alternative optimal weights in the cross-efficiency evaluation.However, in the presence of different secondary goals, the rank assigned to some of decision making units (DMUs) may be non-unique. To overcome the shortcomings, this paper considers the elements of cross-efficiency matrix as interval numbers based on maximum and minimum cross-efficiency that each of the DMUs can achieve. Then, the study using a formula reorders the aggregated interval numbers in each row of the cross-efficiency matrix, and obtains the rank of each of DMUs. The new method removes the problem of the DEA alternative optimal weights in the cross-efficiency evaluation. Moreover, it provides a distinct and unique rank to each of the DMUs. Ultimately, the usefulness of the proposed method in comparison with the previous methods is demonstrated by using a numerical example.

Data envelopment analysis (DEA) has been extended to cross -efficiency evaluation for ranking decision making units (DEA) and eliminating unrealistic weighting schemes.Unfortunately, the nonunique optimal weights problem in DEA has reduced the usefulness of this extended method.Aiming at solving this problem, we first incorporate a target idenification model to get reachable targets for all the DMUs. Then, several secondary goal models are proposed for wights selection considering both desirable and undesirable cross-efficiency targets of all the DMUs. Compared with the traditional secondary goal models, the cross-efficiency targets are improved in that all targets are always reachable for the DMUs. In addition, the proposed models considered the DMUs, willingness to get close to their desirable cross-efficiency targets and to avoid their undesirable cross-efficiency targets simultaneously while the traditional secondary goal models considered only the ideal targets of the DMUs. Since usually some detailed data are available, and they have to figure range. In this paper we extend this model and secondary goals so that is able to calculate the cross efficiency of these conditions.

Conflict intermediate measures in DEA models, especially in constraint and open the black box, is the main difference between traditional DEA and network DEA models. Furthermore, from the application's perspective, intermediate measures aren’t deterministic. So, for measuring the efficiency more precisely, they can be considered as imprecise data. The aim of this paper is introducing a stochastic relational model for measuring overall efficiency that deals with intermediate and outputs as stochastic data. The proposed model is applied for portfolio optimization. An actual data set of 27 Iranian stock industries is applied as numerical example. The result shows that SR-NDEA has better discriminant power than R-NDEA model.

Technique of Data Envelopment Analysis, involved methods which conducted for desirable objective management of Decision Making unit, that is same increasing of efficiency level. An important topic in DEA interpretation is the ranking of DMUs. There exist many methods for it in crisp DEA. In this paper we use of the new metric DTMF that proposed by T.Allahviranloo and M.Adabitabar for fuzzy ranking of all DMUs.

One of the many important decisions organizations must make is project selection. Every project includes an initial plan to run, but not every plan can be implemented as a project. In situations where they lack resources or funds, all different plans must first be able to assess profitability in an accurate way, leading to the selection of a combination of proposals to carry out as projects. This paper provides a method to select the most effective set of proposals while considering the maximum use of available resources. Assessing efficiency is considered by a data envelopment analysis (DEA) model.Note that it is assumed that a vector of limited sources is at hand. This vector of resources can be contained human resource, budget, equipment, and facilities. Here, while reviewing some of the models in the selection project field, a common set of weight approach performance evaluation model for assessing the efficiency of the selected proposals is proposed. In this article it has tried to resolve the problems of former models.

Having a ranking range for each unit allows for assessing the extent to which the choice of DEA weights may affect the ranking derived from the cross-efficiency evaluation and can deal with the most important difficulty of cross-efficiency evaluations (i.e., the possible existence of alternative optimal solutions for the DEA weights, which may lead to different cross-efficiency scores depending on the choice that is made). This may, furthermore, yields some useful information in the case of real life problems. This paper develops the ranking ranges by the use of directional distance functions in calculating cross-efficiency evaluations. In this case, by simultaneously accounting for the inefficiency in inputs and outputs, more comprehensive ranking ranges were achieved in crossefficiency evaluations. A numerical example is presented to show the validity of the proposed procedure by comparing the results with those of the previous approaches.