scholarly journals A Revised Model of the Neutral DEA Model and Its Extension

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Peng Liu ◽  
Li-Fang Wang ◽  
Jian Chang
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Neutral Model ◽  
Dea Model ◽  
Model Based

The neutral data envelopment analysis (DEA) model is an alternative way to determine the weights in DEA cross-efficiency evaluation, while avoiding the difficulty in making a choice between the aggressive and benevolent formulations. However, the weights determined by the neutral model merely make the efficiency of part output bigger than other sets of weights. The neutral model is not able to make the efficiency of each output of the DMU biggest among the favorable weights. This neutral model is not purely “neutral” and not most favorable to the DMU. We proposed a revised model for the neutral model. Based on the idea that the DMU should choose a set of weights to maximize its own efficiency, this paper proposes a new cross-efficiency model. The weights determined by the two models are neutral, neither aggressive nor benevolent.

DEA by sequential exclusion of alternatives in heterogeneous samples

2016 ◽  
Vol 15 (01) ◽  
pp. 5-22 ◽  
Author(s):  
Fuad Aleskerov ◽  
Vsevolod Petrushchenko
Keyword(s):  
Data Envelopment Analysis ◽  
New Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Dea Model ◽  
Economic Applications ◽  
Heterogeneous Samples ◽  
Efficiency Estimation ◽  
Dea Approach ◽  
Russian Universities

Data Envelopment Analysis (DEA) is a well-known nonparametric technique of efficiency evaluation which is actively used in many economic applications. However, DEA is not very well applicable when a sample consists of firms operating under drastically different conditions. We offer a new method of efficiency estimation in heterogeneous samples based on a sequential exclusion of alternatives and standard DEA approach. We show a connection between efficiency scores obtained via standard DEA model and the ones obtained via our algorithm. We also illustrate our model by evaluating 28 Russian universities and compare the results obtained by two techniques.

Energy management in crop production using a novel fuzzy data envelopment analysis model

2018 ◽  
Vol 52 (2) ◽  
pp. 595-617 ◽  
Author(s):  
Mohammad Izadikhah ◽  
Alireza Khoshroo
Keyword(s):  
Data Envelopment Analysis ◽  
Crop Production ◽  
Fuzzy Variable ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Dea Model ◽  
Model Based ◽  
Smoke Pollution ◽  
Fuzzy Dea

Data envelopment analysis is a relatively “data oriented” approach to measure the efficiency of a set of decision making units which transform multiple inputs into multiple outputs. However, some production processes may generate undesirable outputs like smoke pollution or waste. On the other hand, in many situations, such as a manufacturing system, a production process or a service system, inputs and outputs can be considered as a fuzzy variable. Thus, this paper has presented a new non-radial DEA model based on a modification of Enhanced Russell Model (ERM model) in the presence of an undesirable output in a fuzzy environment. Hereafter, a method for solving the proposed fuzzy DEA model based on the concept of alpha cut and possibility approach is presented. A useful stochastic closeness coefficient is also proposed to present a complete ranking. The proposed methodology is applied to evaluate the efficiencies of barley production farms in 22 provinces in Iran.

scholarly journals Environmental Efficiency Evaluation in the Top Asian Economies: An Application of DEA

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 889
Author(s):  
Chia-Nan Wang ◽  
Hoang-Phu Nguyen ◽  
Cheng-Wen Chang
Keyword(s):  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Environmental Efficiency ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Undesirable Output ◽  
Dea Model ◽  
Asian Economies ◽  
Dea Method ◽  
Bad Outputs

Sustainable development has become a global catchphrase in the recent development age. This leads to the growth of various methodologies in evaluating environmental efficiency, such as the Data Envelopment Analysis (DEA) method. The purpose of this study is to propose an extended DEA model, i.e., the undesirable output model, in measuring the relative eco-efficiency scores across nations. The study collected the data of inputs, namely bad outputs and good outputs of the top 20 Asian economies in the period of 2005–2019, and then estimated the environmental efficiency of each country and classified them. The results have shown that there are four nations having higher average environmental efficiency than others. Japan is a good example of sustainable development that simultaneously balances economic development and environmental protection. The study has also discussed possible solutions for improvement to the group of nations with low environmental efficiency. Contributing to applying a novelty extended DEA model, this work recommends a more precise model, taking the weight of outputs into account for further studies.

A synthesized data envelopment analysis model and its application in resource efficiency evaluation and dynamic trend analysis

2017 ◽  
Vol 29 (2) ◽  
pp. 260-280 ◽  
Author(s):  
Sun Meng ◽  
Wei Zhou ◽  
Jin Chen ◽  
Cheng Zhang
Keyword(s):  
Data Envelopment Analysis ◽  
Malmquist Index ◽  
Resource Efficiency ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Analysis Model ◽  
Dea Model ◽  
The Government ◽  
Input Redundancy

Based on the total factor productivity and the resource efficiency, this paper proposes a synthesized data envelopment analysis (DEA) model by using the DEA approach and the Malmquist index. Furthermore, this model is applied to a comprehensive empirical study of the resource efficiency evaluation in China from 2013 to 2015. By introducing some desirable and undesirable factors, we calculate and analyze the whole resource efficiency, the input redundancy ratio, and the output inefficiency ratio of China from 2013 to 2015 based on the synthesized DEA model. Then, we analyze the dynamic trends of the resource efficiency in 31 provinces of China during these three years by applying the corresponding Malmquist index. After that, some interesting conclusions are derived, which are useful for the government. At last, some practical suggestions about improving the resource efficiency of these provinces are provided.

A cross evaluation-based measure of super efficiency in DEA with interval data

Kybernetes ◽  
2016 ◽  
Vol 45 (4) ◽  
pp. 666-679 ◽  
Author(s):  
Qian Yu ◽  
Fujun Hou
Keyword(s):  
Data Envelopment Analysis ◽  
Practical Method ◽  
Interval Data ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Content Type ◽  
Super Efficiency ◽  
Dea Model ◽  
Model Based ◽  
Proposed Model

Purpose – The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values of inputs and outputs, but it cannot handle the imprecise data. The purpose of this paper is to establish a super efficiency interval data envelopment analysis (IDEA) model, an IDEA model based on cross-evaluation and a cross evaluation-based measure of super efficiency IDEA model. And the authors apply the proposed approach to data on the 29 public secondary schools in Greece, and further demonstrate the feasibility of the proposed approach. Design/methodology/approach – In this paper, based on the IDEA model, the authors propose an improved version of establishing a super efficiency IDEA model, an IDEA model based on cross-evaluation, and then present a cross evaluation-based measure of super efficiency IDEA model by combining the super efficiency method with cross-evaluation. The proposed model cannot only discriminate the performance of efficient DMUs from inefficient ones, but also can distinguish between the efficient DMUs. By using the proposed approach, the overall performance of all DMUs with interval data can be fully ranked. Findings – A numerical example is presented to illustrate the application of the proposed methodology. The result shows that the proposed approach is an effective and practical method to measure the efficiency of the DMUs with imprecise data. Practical implications – The proposed model can avoid the fact that the original DEA model can only distinguish the performance of efficient DMUs from inefficient ones, but cannot discriminate between the efficient DMUs. Originality/value – This paper introduces the effective method to obtain the complete rank of all DMUs with interval data.

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