Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste

2021 ◽  
pp. 79-92
Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat ◽  
Kumpanat Chaiphet
Keyword(s):  
Decision Making ◽  
Agricultural Waste ◽  
Ranking Method ◽  
Data Envelopment ◽  
Decision Matrix ◽  
Ranking Models ◽  
Biomass Materials ◽  
Ranking Problems ◽  
Decision Making Units

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

A Robust Data Envelopment Analysis Method with Bounded Data for Ranking Operations Strategies

2015 ◽  
Vol 6 (3) ◽  
pp. 49-64 ◽  
Author(s):  
Mohamad Amin Kaviani ◽  
Mehdi Abbasi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Cement Industry ◽  
Data Envelopment ◽  
Analysis Method ◽  
Interval Numbers ◽  
Complete Ranking ◽  
Bounded Data ◽  
Decision Making Units

This paper introduces a new robust data envelopment analysis (RDEA) approach for analyzing and ranking the organizations' operations strategies. In the proposed RDEA method, pessimistic and optimistic efficiencies of decision making units (DMUs) obtained from the robust counterpart of the envelopment form and the optimistic counterpart of the multiplier form of DEA are introduced. The inputs and outputs data are assumed to be bounded data (interval numbers) in the proposed models. A case study in the cement industry is presented to demonstrate the applicability of the proposed RDEA approach. The results obtained from the authors' proposed RDEA approach is more robust and their method provides a more complete ranking of the DMUs compared to conventional Likert-based DEA model.

Evaluation of Supplier Performance and Efficiency

2017 ◽  
pp. 184-216
Author(s):  
Chandra Sekhar Patro
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Business Environment ◽  
Vital Role ◽  
Data Envelopment ◽  
Supplier Performance ◽  
Decision Making Units

In the present competitive business environment, it is essential for the management of any organisation to take wise decisions regarding supplier evaluation. It plays a vital role in establishing an effective supply chain for any organisation. Most of the experts agreed that there is no one best way to evaluate the suppliers and different organizations use different approaches for evaluating supplier efficiency. The overall objective of any approach is to reduce purchase risk and maximize overall value to the purchaser. In this paper Data Envelopment Analysis (DEA) technique is developed to evaluate the supplier efficiency for an organisation. DEA is a multifactor productivity technique to measure the relative efficiency of the decision making units. The super efficiency method of DEA provides a way, which indicates the extent to which the efficient suppliers exceed the efficient frontier formed by other efficient suppliers. A case study is undertaken to evaluate the supplier performance and efficiency using DEA approach.

scholarly journals Identification and Prioritization of Hazardous Road Locations by Segmentation and Data Envelopment Analysis Approach

1970 ◽  
Vol 25 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Aliasghar Sadeghi ◽  
Esmaeel Ayati ◽  
Mohammadali Pirayesh Neghab
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Regression Models ◽  
Data Envelopment ◽  
The Road ◽  
Black Spots ◽  
Main Factors ◽  
Dea Method ◽  
Decision Making Units

The aim of the present study is the representation of a method to identify and prioritize accident-prone sections (APSs) based upon efficiency concept to emphasize accidents with regard to traffic, geometric and environmental circumstances of road which can consider the interaction of accidents as well as their casual factors. This study incorporates the segmentation procedure into data envelopment analysis (DEA) technique which has no requirement of distribution function and special assumptions, unlike the regression models. A case study has been done on 144.4km length of Iran roads to describe the approach. Eleven accident-prone sections were identified among 154 sections obtained from the segmentation process and their prioritization was made based on the inefficiency values coming from DEA method. The comparisons demonstrated that the frequency and severity of accidents would not be only considered as the main factors for black-spots identification but proper rating can be possible by obtaining inefficiency values from this method for the road sections. This approach could applicably offer decision-making units for identifying accident-prone sections and their prioritizations. Also, it can be used to prioritize intersections, roundabouts or the total roads of the safety organization domain.

Multi-Criteria Decision Making for Ranking Decision Making Units

2018 ◽  
Vol 6 (1) ◽  
pp. 17-36
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Traditional Method ◽  
Multiple Criteria Decision Making ◽  
Rank Correlation ◽  
Ranking Method ◽  
Spearman’S Rank Correlation ◽  
Ranking Models ◽  
Spearman’S Rank Correlation Coefficient ◽  
One Step ◽  
Decision Making Units

There is no doubt the Data Envelopment Analysis (DEA) is a powerful method for the efficiency evaluation of Decision Making Units (DMUs) with multiple inputs and outputs. Despite its usefulness, DEA has some notable limitations. A significant drawback with this approach is that inability to fully rank the DMUs. In the extant literature, different methods for this purpose have been suggested. While, in the traditional method the first step for the DEA approach is used, and results of this step are input for the DEA ranking method in the second step. To reduce the computational complexity of the traditional method, a new Multiple Criteria Decision Making (MCDM) approach is proposed in this article. In the proposed approaches, one step can achieve full ranking for all DMUs. The results show that although out of 20 DMUs are first in the final ranking ordered by the DEA, the author proposed methods can consider full ranking. Agreement of the proposed methods with the existing approaches are measured by the Spearman's rank correlation coefficient technique. The findings of this study reveal that TOPSIS, Neo-TOPSIS, and AHP ranking results are consistent with the DEA ranking method. Therefore, these proposed methods appear as the possible alternatives to the DEA and DEA ranking models.

scholarly journals A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

2019 ◽  
Vol 11 (7) ◽  
pp. 2059 ◽  
Author(s):  
Jiyoung Lee ◽  
Gyunghyun Choi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Hybrid Electric Vehicles ◽  
Ranking Method ◽  
Data Envelopment ◽  
Discriminating Power ◽  
Network Method ◽  
Representation Technique ◽  
Using Data ◽  
Decision Making Units

Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.

scholarly journals Hybrid cross-efficiency approach based on Ideal and Anti-Ideal points and the CRITIC method for ranking decision-making units: A case study on ranking the methods of rice weevil disinfestation

2021 ◽  
Vol 10 (3) ◽  
pp. 375-392
Author(s):  
Pariwat Nasawat ◽  
Sukangkana Talangkun ◽  
Sirawadee Arunyanart ◽  
Narong Wichapa
Keyword(s):  
Decision Making ◽  
Evaluation Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
New Approach ◽  
Comprehensive Method ◽  
Ideal Points ◽  
The Cross ◽  
Decision Making Units

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

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