International Journal of Data Envelopment Analysis
Volume:8 Issue: 4, Dec 2020

< p>In this paper, first the DEA-RA (integration of DEA with Ratio Analysis) output and input oriented is proposed. Then, by using the fully fuzzy problem and the problem that only the parameters are fuzzy, the DEA-RA joint model has been proposed. Therefore the lexicographic approach and the α-cut approach have been used for these two types of the problems and evaluation of research projects. DEA-RA models behave similarly to DEA models. In this study, the input and output parameters are not ratio, but if the input to output ratios and vice versa are defined, DEA-RA models can also be a criterion for calculating the efficiency and evaluation of units.

Inverse DEA (InvDEA) models put one step forward, in contrast with the DEA models by estimating required input level for producing a perturbed output level with the current efficiency status. In many real world applications, decision-makers face non-discretionary factors which can be hardly controlled by the Decision Making Units (DMUs). However, these types of factor are not dealt in the inverse DEA problems. Thus, the current covers the research methodological gap of the literature by developing mathematical foundation of the InDEA models capable of dealing with non-discretionary factors. To do this end, an MOLP model along with its required constraints is developed to be linked with the developed models. A numerical example and a real-world case study are provided to illustrate the proposed models and demonstrate their applicability and validity for the real world problems.

Resource allocation is one of the most complicated managers' concerns, which forces them to seek novel methods and approaches. The fixed-cost (FC) allocation among different decision-making units is one such problem. That has led to considerable research in this regard. Numerous methods have been proposed, that the most important of them is Data Envelopment Analysis (DEA). Fixed-Cost allocation problem is more reflected in project portfolio management. The management has to allocate a common cost between several different projects. However, due to the nature of the project, the previously proposed methods are insufficiently adapted to the project execution realities. In this research, a novel method is proposed to allocate FC aligned with project management methods. For this purpose, the current situation of projects is first evaluated using the Earned Values Management method. Then projects which are out of scope are identified. Inverse Data Envelopment Analysis estimates the required costs to keep them back into their scope on the assumption that the project's efficiency remains constant. Finally, by using non-linear programming, the fixed-cost allocates optimally. In order to illustrate the proposed method, a real case example is presented.

Today, the network has an important role in a system. Determining the efficiency of the system as well as the efficiency of the sub-sections of a network helps to effectively manage the network. If the optimal value of the sub-sections is not unique, an efficiency range can be obtained for them. In this paper, the performance of a network is analysis with the help of data envelopment analysis and performance intervals are determined for each of the sub- sections of the network. With help decomposition the performance of a network structure, the performance of its components was obtained. The relationship between component performance and interval value was calculated with the overall efficiency network.

Measuring the productivity of banks, accompanied by analyzing and comparing their performance, have important roles in improving their productivity and efficiency so that they make up the backbone of the plans of banks. Initially, the Malmquist Productivity Index (MPI), one of the indicators of productivity measurement, is introduced. Hence, the paper describes how to obtain this index using the Data Envelopment Analysis (DEA). Then, the performance of Mellat Bank branches in Lorestan province is evaluated using the MPI to identify the efficient branches and the critical indicators of each branch, in order to increase their productivity

Data covering analysis is a technique for evaluating the performance of homogeneous decision-making units. In evaluating the performance of units, it is necessary the efficiency and ranking of units be done (be calculated). Ranking decision-making units is based on efficiency. According to the existence of various efficacies in the applied examples, different techniques have been developed for ranking, that in this study, cross efficiency method has been used. This technique has been developed for fuzzy input and output modes. While data are fuzzy, cross efficiency table has been calculated inaccurately and table entries are fuzzy numbers, so the average (mean) of calculated efficiency in the table is also a fuzzy number. Since comparing the averages of obtained efficiencies ranks the decision-making units, it is necessary to propose a solution for this comparison. In this research, after providing the required grounds with assistance of models and doing the necessary changes in them, the averages of efficiency are compared by assistance of fuzzy ranking function. And with the knowledge that every decision-making unit that has better average (mean) is more efficient, decision-making units are ranked.

Nowadays, performance measurement is carried out through two types of methods parameterized and non-parameterized. This research focuses on non-parametric methods, specifically those that utilize mathematical programming, to assess the performance of cement companies in the Tehran Stock Exchange (TSE) market. The coverage analysis method used in this study provides an advantage over existing methods in that it can evaluate the performance of units with multiple inputs and outputs that are not interchangeable (UMIMO). The evaluation of performance was carried out using two assumptions: "Performance against constant scale" and "performance against variable scale", based on the inputs and outputs of cement companies in the TSE. The study examined 22 cement producing companies, finding 7 efficient units according to the CCR method and 11 efficient units according to the BCC method.