Traditional Data Envelopment Analysis Methods Research Articles

A simulation-based DEA approach for multiple criterion decision-making problems with uncertain mixed-criteria values

In ex-ante decision scenarios, predicting criterion values accurately is difficult for decision makers (DMs). Inconsiderable work is normally required for measuring criteria by uncertain random values or ordinal values. However, in the classical data envelopment analysis (DEA) model, criterion values are the constants that limit the application of the classical DEA model in ex-ante decision scenarios. This paper presents a simulation-based DEA approach, which captures random and ordinal criterion values by a simple and direct simulation-based approach. The approach includes three steps. In the first step, Monte Carlo simulation methods are used to convert uncertain random values or ordinal values into cardinal data. In the second step, we use traditional DEA methods to compute the efficiency score of decision-making units (DMUs). In the third step, we ranked all DMUs by calculating the DEA-efficient acceptability of each DMU in multiple simulations and then selected the optimal DMU. The proposed approach is illustrated by experimental examples and a case study of a municipal wastewater treatment system.

Multi-sector environmental efficiency and productivity: A general Leontief optimization method

For efficiency evaluation purposes, a multi-sector economy is always regarded as a “black box” in which the linkages among sectors are not considered. This leads to biased efficiency evaluation and failure to identify the sources of inefficiency. To solve these problems, this paper proposes a general Leontief optimization method for environmental performance estimation. The proposed method can assess the environmental benefits that result from the adjustment of input coefficients and environmental factor intensities. Moreover, this study proposes two models under different elasticity of input substitution assumptions. The difference in environmental efficiency between the two assumptions is defined here as the substitution efficiency. Further, a new Luenberger environmental productivity indicator is constructed that contains a new element reflecting the effect of input substitution. To demonstrate the new method, a numerical example is used to compare the estimation results between the general Leontief optimization method and the traditional data envelopment analysis method. Then, the environmental performance associated with CO2 emissions and energy consumption in China is studied over two years, i.e., 2012 and 2017. By fully opening the “black box” of how to improve environmental efficiency, this study provides a reference with regard to cost-effective specific measures for policymakers in China.

Super efficiency SBM-DEA and neural network for performance evaluation

The traditional data envelopment analysis (DEA) method used for performance evaluation has inherent problems such as being easily affected by statistical noise in data. Furthermore, when new evaluation units are added, the performance of all the original units must be re-measured, which restricts the evaluation efficiency. In this study, machine learning algorithms were applied to make up for the shortcomings of the data envelopment analysis method. First, a super-efficiency SBM model was used to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier. After 15 machine learning algorithms were compared, BPNN demonstrated the best performance, and a SuperSBM-DEA-BPNN model was eventually established. The new model has the following advantages: First, compared with the traditional data envelopment analysis method, the absolute effective frontier displays better evaluation; second, compared with the data envelopment analysis and neural network fusion outlined in the previous literature, the new model can better overcome the problems associated with data envelopment analysis, thereby improving the fusion efficiency. Taking the innovation efficiency evaluation of China's regional rural commercial banks for instance, the new model is proven to be more applicable and offers more effective management tools to improve efficiency. On the whole, the new model not only provides a stable performance evaluation tool but also facilitates comparison, which has good application significance for organizations.

Assessing the Static and Dynamic Efficiency of Scientific Research of HEIs China: Three Stage DEA–Malmquist Index Approach

Higher education institutions (HEIs) are the key to the economic and social development of a country. However, the recent advancements of higher education institutions’ universities in China have become a pivotal factor contributing to their swift growth. Considering the impact of the external environment, applying a three-stage data-envelopment analysis (DEA) and the Malmquist index method, we evaluated the static and dynamic efficiency of input–output data of scientific research produced by universities directly under the Ministry of Education in the period of 2010 to 2017. Results showed that the three stage DEA model is more accurate than the traditional DEA method for measuring the efficiency of scientific research input and universities’ output. The overall efficiency of universities’ scientific research activities increased at an average annual rate of 3.7% from 2010 to 2017. Further analysis showed that the optimization of the scale was the primary internal factor able to promote the efficiency of scientific research in universities. Technological progress slightly diminishes the efficiency of scientific research in universities, which should, therefore, take measures to improve pure technical efficiency to enlarge the scale of their scientific research according to the characteristics of scientific research efficiency. Such steps will strengthen internal motivation towards scientific research efficiency in universities.

Biofuel logistics network scheme design with combined data envelopment analysis approach

This paper studies the biofuel logistics network scheme (BLNS) design problem dealing with several decision variables, such as the biofuel facility location, allocation, delivering harvested biomass stocks to biofuel producing facilities, and distributing biofuels to the stations. Since building the biofuel facilities requires a huge amount of investments, designing an efficient BLNS will be essential to attract potential investors. We formulate the design problem as a multi-objective programming (MOP) model. Solving the MOP model would yield various network schemes based upon the weight given to each objective/goal. Data envelopment analysis (DEA) method could be applied to evaluate the efficiency of each BLNS generated by solving the MOP model. Several approaches based on the traditional DEA method emerge to overcome a critical weakness regarding discriminating power. This paper combines three well-known DEA methods to make the most use of each method’s strengths and to evaluate and identify the more efficient network schemes. Through a case study for South Carolina, we observe that the proposed procedure performs well in terms of designing efficient and robust BLNSs. The proposed procedure would enable the decision-makers to have more choices for BLNSs to consider before finally selecting the best scheme regarding efficiency, practicality, and feasibility.

Integration of network data envelopment analysis and decision‐making trial and evaluation laboratory for the performance evaluation of the financial holding companies in Taiwan

Data envelopment analysis (DEA) has been constantly used to measure the technical efficiency of decision‐making units (DMUs). However, the major problem of traditional DEA methods is that they do not consider the possible intermediate effects. Recently, many papers have applied network DEA models to evaluate the efficiency scores. However, the linking activity of DMUs is still hard to be recognized. Hence, we employ DEMATEL to obtain the linking activity of DMUs. Our empirical research shows that the proposed method can soundly deal with the purpose of identifying the relationship between variables and derive the reasonable result in network DEA.

Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes

The complex chemical process is a high pollution and high energy consumption industrial process. Therefore, it is very important to analyze and evaluate the energy and environment efficiency of the complex chemical process. Data Envelopment Analysis (DEA) is used to evaluate the relative effectiveness of decision-making units (DMUs). However, the traditional DEA method usually cannot genuinely distinguish the effective and inefficient DMU due to its extreme or unreasonable weight distribution of input and output variables. Therefore, this paper proposes an energy and environment efficiency analysis method based on an improved environment DEA cross-model (DEACM) method. The inputs of the complex chemical process are divided into energy and non-energy inputs. Meanwhile, the outputs are divided into desirable and undesirable outputs. And then the energy and environment performance index (EEPI) based on the cross evaluation is used to represent the overall performance of each DMU. Moreover, the improvement direction of energy-saving and carbon emission reduction of each inefficiency DMU is quantitatively obtained based on the self-evaluation model of the improved environment DEACM. The results show that the improved environment DEACM method has a better effective discrimination than the original DEA method by analyzing the energy and environment efficiency of the ethylene production process in complex chemical processes, and it can obtain the potential of energy-saving and carbon emission reduction of ethylene plants, especially the improvement direction of inefficient DMUs to improve energy efficiency and reduce carbon emission.

Efficiency evaluation based on data envelopment analysis in the big data context

Data envelopment analysis (DEA) is a self-evaluation method which assesses the relative efficiency of a particular decision making unit (DMU) within a group of DMUs. It has been widely applied in real-world scenarios, and traditional DEA models with a limited number of variables and linear constraints can be computed easily. However, DEA using big data involves huge numbers of DMUs, which may increase the computational load to beyond what is practical with traditional DEA methods. In this paper, we propose novel algorithms to accelerate the computation process in the big data environment. Specifically, we firstly use an algorithm to divide the large scale DMUs into small scale and identify all strongly efficient DMUs. If the strongly efficient DMU set is not too large, we can use the efficient DMUs as a sample set to evaluate the efficiency of inefficient DMUs. Otherwise, we can identify two reference points as the sample in the situation of just one input and one output. Furthermore, a variant of the algorithm is presented to handle cases with multiple inputs or multiple outputs, in which some of the strongly efficient DMUs are reselected as a reduced-size sample set to precisely measure the efficiency of inefficient DMUs. Last, we test the proposed methods on simulated data in various scenarios.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

A new look at measuring sustainability of industrial parks: a two-stage data envelopment analysis approach

An industrial park is a community of manufacturing and service businesses located together in a specific area. The sustainable industrial park is strategically significant for sustainable development of countries. In this paper, a gradual efficiency improvement data envelopment analysis (DEA) model is proposed to (i) measure sustainability of industrial parks, (ii) classify follower and pioneer industrial parks regarding the environmental performance, and (iii) direct the followers toward pioneers to gradually (not at once) improve their performance and make feasible improvement plans. The proposed method identifies decision-making units (DMUs) on most productive scale size (MPSS) region, and then projects other DMUs onto this region. The proposed model is extended to evaluate performance of a two-stage network structures where outputs from the first stage become inputs to second stage in addition to inputs of first stage and outputs of second stage. The proposed approach is applied to measure the sustainability of 31 Iranian industrial parks. Despite traditional DEA methods, the results indicate that inputs (outputs) should not be necessarily decreased (increased) to project the industrial parks onto the MPSS region.

Technical Efficiency Measurement and Inverse 𝔹-Convexity: Moroccan Travel Agencies

This paper uses an innovative data envelopment analysis (DEA) model, the inverse 𝔹-convex model (Briec and Liang, 2011). This approach is used to measure the technical efficiency of Moroccan travel agencies. The travel agencies are ranked according to their efficiency over the period 2006–2008. The methodology is found to provide more accurate measurements of the travel agencies' efficiency. The paper also makes methodological and managerial contributions. Technical efficiency is analysed by using the inverse 𝔹-convexity model, comparing and contrasting it with traditional DEA methods. A key advantage of such an approach is that, on the one hand, traditional convexity assumption is relaxed and, on the other hand, it underlines potential complementarities of inputs. Finally, policy implications are derived.

An empirical study of Iranian regional airports using robust data envelopment analysis

Article history: Received 1 Feb 2010 Received in revised form 20 March 2010 Accepted 1 April 2010 Available online 7 April 2010 Data Envelopment Analysis (DEA) has been one of the most important tools on measuring the relative efficiency of different similar units such as transportation systems using terminals, airports, etc. In this study, we perform an empirical analysis on Iranian airports based on DEA methods to measure the efficiencies of various airports. One of the primary issues on many traditional DEA methods is that the data are almost always contaminated with noise. We use a DEA method which could handle the uncertainty associated with input and output data. The results of this comprehensive study show that most of the active airlines are practically inefficient and the government could significantly increase the efficiencies of the airports by setting new regulations and rules. © 2010 Growing Science Ltd. All rights reserved.

Risk‐adjusted efficiency and risk aversion in the agricultural banking industry

PurposeThe purpose of this paper is to examine the performance of the agricultural banking industry using both traditional and risk‐adjusted non‐parametric efficiency measurement techniques. In addition to computing efficiency scores, the risk preference structure of the agricultural banking industry is examined.Design/methodology/approachThe paper used data envelopment analysis (DEA) to examine the efficiency of agricultural banks in the year 2001. Standard cost efficiency is computed and compared to both profit and risk‐adjusted profit efficiency scores. The risk‐adjustment is a modification of traditional DEA wherein firm preferences are represented via a mean‐variance criterion. The risk‐adjusted technique also provides estimates of firm level risk aversion.FindingsResults from the traditional approach that does not account for risk indicate a low degree of efficiency in the banking industry, while the risk‐adjusted approach indicates banks are much more efficient. On average, 77 percent of the inefficiency identified by the standard DEA formulation is actually attributable to risk averse behavior by the firm. In addition, most banks appear to be substantially risk averse.Research limitations/implicationsThe risk‐adjusted DEA technique used in this study should be applied to other, diverse data sets to examine its performance in a broader context.Practical implicationsResults from this study support the idea that traditional DEA methods may mischaracterize the level of efficiency in the data if agents are risk averse. In addition, the paper outlines a practical method for deriving firm level risk aversion coefficients.Originality/valueThis paper sheds light on the agricultural banking industry and illustrates the power of a new efficiency and risk analysis technique.

Evaluating Supplier Performance Using DEA and Piecewise Triangular Fuzzy AHP

Data envelopment analysis (DEA) has been widely applied in evaluating multicriteria decision making problems, which have multi-input and multi-output. However, the traditional DEA method does neither take the decision maker’s subjective preferences to the individual criteria into consideration nor rank the selected options or decision making units (DMUs). On the other hand, Satty’s analytical hierarchy process (AHP) was established to rank options or DMUs under multi-input and multi-output through pairwise comparisons. However, in most cases, the AHP pairwise comparison method is not perfectly consistent, which may give rise to confusions in determining the appropriate priorities of each criterion to be considered. The inconsistency implicates the fuzziness in generating the relative important weight for each criterion. In this paper, a novel method, which employs both DEA and AHP methods, is proposed to evaluate the overall performance of suppliers’ involvement in the production of a manufacturing company. This method has been developed through modifying the DEA method into a weighting constrained DEA method by using a piecewise triangular weighting fuzzy set, which is generated from the inconsistent AHP comparisons. A bias tolerance ratio (BTR) is introduced to represent the varying but restrained weighting values of each criterion. Accordingly, the BTR provides the decision maker a controllable parameter by tightening or loosening the range of the weighting values in evaluating the overall performance of available suppliers, which in hence, overcomes the two weaknesses of the traditional DEA method.

A PERFORMANCE EVALUATION MODEL BASED ON AHP AND DEA

ABSTRACT This research uses the concept of the Analytical Hierarchy Process (AHP) method and fuzzy set theory to modify the CCR model of the Data Envelopment Analysis (DEA) method to develop a more effective performance evaluation method which can be used to evaluate the performance of a smaller number of business units, which is usually prohibited in the traditional DEA method. The proposed method maintains all the important features of the original DEA method like the objectivity by using data to analyze an evaluation problem and the ability to suggest the improvement directions for each business unit under evaluation. In order to prove the applicability of the proposed method, a case study for the selection of suppliers for the key component of a LCD manufacturing company is given. The result shows that it can be used to choose among a small number of suppliers in a very efficient way and also can provide improvement suggestions for each supplier.