A DEA-BASED METHOD OF STEPWISE BENCHMARK TARGET SELECTION WITH PREFERENCE, DIRECTION AND SIMILARITY CRITERIA (original) (raw)
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Sustainability, 2016
Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency DEA, K-means clustering and context-dependent DEA methods to minimize resource improvement pattern inconsistency in the selection of the intermediate benchmark targets (IBTs) of an inefficient DMU. The specific advantages and overall effectiveness of the proposed method were demonstrated by application to a case study of 34 actual container terminal ports and the successful determination of the stepwise benchmarking path of an inefficient DMU.
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2012
Data Envelopment Analysis (DEA) is a non-parametric approach to operations research for assessing the relative efficiencies of a set of peer units, called Decision Making Units (DMUs), with multiple inputs and multiple outputs. DEA provides a fair benchmarking tool that includes a technical efficiency score for each DMU, a technical efficiency reference set with peer DMUs, a target for a technically inefficient DMU, and information detailing by how much inputs can be decreased or outputs can be increased to improve performance of DMUs. In this paper, we compare DEA models to benchmark technically inefficient DMUs, and prove that popular models like the Slack-Based Measure (SBM) and Charnes, Cooper and Rhodes (CCR) may not give the acceptable results for benchmarking technically inefficient DMUs as strong as the weighted additive (ADD) model. The study also warns against applying the conventional DEA models for most of applications and suggests using the Kourosh and Arash Method to (KAM) assess the performance evaluation of DMUs.
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Expert Systems with Applications, 2011
One of the existing DEA methods' limitations noted in the literature lies in the process of benchmarking of reference targets for inefficient DMUs. Difficulties arising in this process can be summarized to three aspects. First, the reference target might be a hypothetical DMU that does not actually exist (it is difficult and indeed unrealistic to learn from such a DMU). Second, the reference set of an inefficient DMU often has multiple efficient DMUs making it difficult to benchmark multiple best-practice DMUs simultaneously. Third, it is quite impossible for an inefficient DMU to achieve its target's efficiency in a single step, especially when the inefficient DMU is far from the efficient frontier. In order to overcome these difficulties, we propose, in place of the selection of benchmarked DMUs on the efficient frontier, a method of selecting effective benchmarking paths that direct an inefficient DMU to its target on the efficient frontier in an implementable and realistic way. The proposed method was designed based on the idea of the context-dependent DEA proposed by Seiford and Zhu (2003). It starts by clustering DMUs into several layers according to their efficiency scores, and then establishes a benchmarking path across the sequence of layers. Among the DMUs in the next layer, the most preferable one is selected as the next benchmark target, based on three criteria: attractiveness, progress, and infeasibility. We tested the proposed method by applying it to the evaluation of the relative efficiency of operations of 26 container terminals located in Asia.
A new approach for ranking efficient DMUs with data envelopment analysis
World Journal of Engineering, 2020
Purpose Classical models of data envelopment analysis (DEA) calculate the efficiency of decision-making units do not differentiate between efficient units. The purpose of this paper is to present a new method for ranking efficient units and compare it with the other methods presented in this field. Design/methodology/approach In this paper, a new method is presented for ranking efficient units. To validate the proposed method, a real case, which was studied by Li et al. (2016) is examined and the rankings of the efficient units are compared with four other methods including the Andersen and Petersen’s super-efficiency, game theory and the concept of Shapley value and the technique for order of preference by similarity to ideal solution methods. Findings The results show that there is a high correlation between the rankings of efficient units obtained by the new proposed method and the other methods such as Andersen and Petersen’s super-efficiency, game theory and Shapley value metho...
Annals of Operations Research, 2016
Data Envelopment Analysis (DEA) is a non-parametric technique for evaluating a set of homogeneous decision-making units (DMUs) with multiple inputs and multiple outputs. Various DEA methods have been proposed to rank all the DMUs or to select a single efficient DMU with a single constant input and multiple outputs [i.e., without explicit inputs (WEI)] as well as multiple inputs and a single constant output [i.e., without explicit outputs (WEO)]. However, the majority of these methods are computationally complex and difficult to use. This study proposes an efficient method for finding a single efficient DMU, known as the most efficient DMU, under WEI and WEO conditions. Two compact forms are introduced to determine the most efficient DMU without solving an optimization model under the DEA-WEI and DEA-WEO conditions. A comparative analysis shows a significant reduction in the computational complexity of the proposed method over previous studies. Four numerical examples from different contexts are presented to demonstrate the applicability and exhibit the effectiveness of the proposed compact forms.
Applied Mathematical …, 2012
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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.
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.
A multi-objective approach to determine alternative targets in data envelopment analysis
Journal of The Operational Research Society, 2004
The choice for radial projections of classic data envelopment analysis (DEA) models, resulting in a number of projections onto the Pareto-inefficient portion of the frontier, has been seen lately as a disadvantage in DEA. The search for a non-radial projection method resulted in developments such as preference structure models. These models consider a priori preference incorporation, using weights in the search for the most preferred efficient target, although presenting some implementation difficulties. In this paper, we propose a multi-objective approach that determines the bases for a posteriori preference incorporation, through individual projections of each variable (input or output) as an objective function, thus allowing one to obtain a target at every extreme-efficient point on the frontier. This multi-objective approach is shown to be equivalent to the preference structure models, yet presenting some advantages, such as the mapping of the possible weights, assigned to partial efficiencies of an observed unit, in order to reach a specific target.
Practical benchmarking in DEA using artificial DMUs
Journal of Industrial Engineering International, 2018
Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or reference point for these efficient units. Impracticability of these benchmarks under environmental conditions is another challenge of benchmarking by DEA. The current study attempts to extend basic models for benchmarking of efficient units under practical conditions. To this end, we construct the practical production possibility set (PPPS) by employing the concept of artificial decision-making units and adding these decisionmaking units to the production possibility set (PPS) such that these artificial units satisfy all environmental constraints. Then, the theorems related to PPPS and their proofs are provided. Moreover, as a secondary result of this study, efficient units can be ranked according to their practical efficiency scores.