Group efficiency analysis in decision processes: a data envelopment analysis approach (original) (raw)
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Journal of the Operational Research Society, 2018
Traditional Data Envelopment Analysis (DEA) evaluates the relative efficiency of a set of homogeneous decision making units (DMUs) regarding multiple inputs and outputs. An important implication of the DEA is dealing with applications wherein the internal structures of DMUs are known, specifically those that have a network framework. In some situations the assumption of homogeneity among the internal of DMUs is violated; for instance, when a set of universities comprises DMUs but not all of them have the same faculties. This paper proposes a DEA-based methodology to deal with the problem of evaluating the relative efficiencies of a set of DMUs whose internal structures are nonhomogeneous. It is shown that the overall efficiency of each DMU could be evaluated through two stages; in the first stage subgroup efficiency scores are derived and the second one evaluates the overall efficiency score of each DMU using a weighted average of the subgroups efficiency scores obtained in stage 1. To show the practical aspects of the newly developed model, it is applied to a set of hypothetical data-set in addition to a real data-set on bank industry.
Group performance evaluation, an application of data envelopment analysis
Journal of Computational and Applied Mathematics, 2009
The contribution of this paper is to provide an approach for evaluating the performance of a group of decision making units (DMUs) based on the production technology. Group evaluation is an application of data envelopment analysis (DEA). DEA uses linear programming to provide a suitable technique to estimate a multiple-input/multipleoutput empirical efficient function. This paper applies group evaluation to evaluate the performance of Iranian commercial banks.
Efficiency improvement of decision making units: a new data envelopment analysis model
International Journal of Mathematics in Operational Research, 2015
The main goal of this paper is to develop a new data envelopment analysis (DEA) model to use optimal weights of each decision making unit (DMU) to improve its relative efficiency aligned with other DMUs. In spite of the vast amount of studies in this area and related tools and techniques, current literature deploys the optimal weights of DMUs to calculate the DMUs' relative efficiency and benchmarking for each DMU is less investigated. In order to fill this gap, this paper proposes a model for obtaining benchmark(s) for each DMU. Furthermore, a numerical example is used to illustrate the capability of the proposed model.
ENHANCING ESTIMATION OF EFFICIENCY OF ECONOMIC ENTITIES: DATA ENVELOPMENT ANALYSIS
BSEU, 2021
The objective of this paper is to describe the Data Envelopment Analysis technique (DEA) used to measure the relative efficiency of decision-making units (DMUs), description of the DEA models and processes, as well as analysis of the technique concerned. The paper shows that DEA plays an important role in measuring relative efficiency and works well even with a small sample of organizations. In accordance with the technique, efficiency and productivity are measured by computing the output to input ratio. The DEA method is used throughout the world by various researchers for evaluating efficiency of different organizations, such as banks, universities and hospitals in different countries.
Applied Mathematical …, 2012
The inefficient DMUs are usually arranged after the technical efficient ones by DEA methods, however, it is possible that a technical efficient DMU neither be efficient nor be more efficient than some inefficient ones. This study distinguishes between the terms 'technical efficiency' and 'efficiency' and demonstrates that the technical efficiency is a necessary condition for being efficient and it is not an enough condition to call a DMU as efficient DMU. The study identifies the definitions of those terms and gives a new strong method to characterize efficient DMUs among the technical efficient ones. The new method, although, avoids the need for recourse to prices, weights or other assumptions between inputs and outputs of DMUs, it is also able to consider the prices and weights. A numerical example is also characterized the worth and benefits of the new proposed model in comparison with all current DEA models.
A Complete Efficiency Ranking of Decision Making Units in Data Envelopment Analysis
1999
The efficiency measures provided by DEA can be used for ranking Decision Making Units (DMUs), however, this ranking procedure does not yield relative rankings for those units with 100% efficiency. Andersen and Petersen have proposed a modified efficiency measure for efficient units which can be used for ranking, but this ranking breaks down in some cases, and can be unstable when one of the DMUs has a relatively small value for some of its inputs. This paper proposes an alternative efficiency measure, based on a different optimization problem that removes the difficulties.
Measuring the multi-component efficiency with shared inputs and outputs in data envelopment analysis
Applied Mathematics and Computation, 2004
In many applications of data envelopment analysis (DEA), the existing models are designed to obtain a single efficiency measure. However, in many real situations, the units under consideration may perform several different functions or can be separated into different components. In these cases, some inputs are often shared among those components and all components are involved in producing some outputs. In this paper, bank branch efficiency is analyzed using data from 31 bank branches in Iran. First, a DEA-efficiency analysis of multi-component DMU s is introduced. Secondly, by grouping the branches according to their organizational designation, their efficiency is measured.
Data Envelopment Analysis and Performance Measurement
2014
Data Envelopment Analysis (DEA) which is applied to evaluate the relative efficiency of decision making units (DMU), is a mathematical programming approach. The efficiency in the classical DEA is "the ratio of the sum of the weighted outputs to the sum of weighted inputs". In order to obtain the maximum efficiency score for each DMU under evaluation, different weights are assigned to the inputs and outputs of the DMU. Classical DEA models allow weight flexibility. Thus, zero weights can be assigned to some important inputs and outputs of the DMU. In this case, such inputs and outputs will be ignored in the evaluation and will be found unrealistic results. Weight restrictions are utilized to eliminate the problem. Input and output variables in the production process are associated with the degree of correlations between these variables. Previous papers didn't consider the relationship between inputs and outputs. In this study, the weights are defined by correlations between input and output variables. So, the new DEA models constrained with correlation coefficients (CCRCOR and BCCCOR) are developed. The CCRCOR and BCCCOR models and other known DEA models were applied on some datasets in the literature. The results were compared with the Spearman rank test. According to the results, the CCRCOR and BCCCOR models provided a more balanced weight distribution than the other models.
Applied Mathematics and Computation, 2005
The assumption of classical Data Envelopment Analysis (DEA) models is based on complete homogeneity of Decision Making Units (DMUs). The objective of this paper is to propose a method of determining relative efficiency of slightly non-homogeneous DMUs by using DEA. First missing values are inserted by series mean. Then relative weights of DMUs are measured by Analytic Hierarchy Process (AHP) and finally relative efficiency of DMUs is computed by chance-constrained DEA. A case study demonstrates the application of the proposed method.
2015
In data envelopment analysis, the relative efficiency of a decision-making unit (DMU) is defined as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the DMUs to freely allocate weights to their inputs/outputs. However, this measure may not reflect a true efficiency of a DMU because some of its inputs/outputs may not contribute reasonably in computing the efficiency measure. Traditionally, to overcome this problem weights restrictions have been imposed. But, an approach for solving this problem by inclusion of some unobserved DMUs, obtained via a process with four steps, has been proposed in 2004. These unobserved DMUs are created by adjusting the output levels of certain observed relatively efficient DMUs. The method used in this research is regarded for DMUs that are operating under a constant return to scale (CRS) technology with a single input multi-output context. This method is implemented for 47 branches of bank Maskan in northeast of Teh...