A NEW RANKING APPROACH WITH A MODIFIED CROSS EVALUATION MATRIX

2013 ◽  
Vol 30 (04) ◽  
pp. 1350008 ◽  
Author(s):  
BYUNG HO JEONG ◽  
CHANG-SOO OK
Keyword(s):  
Rank Order ◽  
Distance Measures ◽  
Data Envelopment ◽  
Ranking Methods ◽  
Ranking Problem ◽  
Dea Models ◽  
The Difference ◽  
Ranking Problems ◽  
Evaluation Matrix

Cross evaluation matrix was suggested to resolve a ranking problem in the data envelopment analysis (DEA) context. The cross evaluation matrix is composed of simple efficiency and cross-efficiency (CE) values of decision making units (DMUs). However, simple efficiency cannot discriminate efficient DMUs because of the nature of basic DEA models. To make complete use of the efficiency information of DMUs, a modified cross evaluation matrix is proposed. The modified matrix consists of super-efficiency (SE) values for diagonal elements and CE values for nondiagonal elements. As the efficiency values are not limited to "1" in SE approach, the proposed matrix can explain the difference of efficiency of efficient DMUs. The proposed matrix can be more accurate than the original cross evaluation matrix. Consequently, the rank order of DMUs generated by the suggested matrix reflects differences in relative efficiency of DMUs. A numerical example is given to show the superiority of the proposed approach. This is done by comparing with other available ranking methods in the DEA context. Several distance measures are utilized to compare rank consistency of the ranking methods. Finally, a case study is presented to explain how our approach is applied to real ranking problems.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

scholarly journals Dealing with Interval DEA Based on Error Propagation and Entropy: A Case Study of Energy Efficiency of Regions in China Considering Environmental Factors

2015 ◽  
Vol 3 (6) ◽  
pp. 538-548 ◽  
Author(s):  
Jianping Fan ◽  
Weizhen Yue ◽  
Meiqin Wu
Keyword(s):  
Energy Efficiency ◽  
Environmental Factors ◽  
Error Propagation ◽  
Interval Data ◽  
Data Envelopment ◽  
Exact Data ◽  
Dea Models ◽  
Traditional Approaches ◽  
Decision Making Units

AbstractThe conventional data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) consuming multiple inputs to produce multiple outputs under the assumption that all the data are exact. In the real world, however, it is possible to obtain interval data rather than exact data because of various limitations, such as statistical errors and incomplete information, et al. To overcome those limitations, researchers have proposed kinds of approaches dealing with interval DEA, which either use traditional DEA models by transforming interval data into exact data or get an efficiency interval by using the bound of interval data. In contrast to the traditional approaches above, the paper deals with interval DEA by combining traditional DEA models with error propagation and entropy, uses idea of the modified cross efficiency to get the ultimate cross efficiency of DMUs in the form of error distribution and ranks DMUs using the calculated ultimate cross efficiency by directional distance index. At last we illustrate the feasibility and effectiveness of the proposed method by applying it to measure energy efficiency of regions in China considering environmental factors.

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.

IMPROVED COMMON WEIGHT DEA-BASED DECISION APPROACH FOR ECONOMIC AND FINANCIAL PERFORMANCE ASSESSMENT

2019 ◽  
Vol 26 (2) ◽  
pp. 430-448
Author(s):  
E. Ertugrul Karsak ◽  
Nazli Goker
Keyword(s):  
Decision Making ◽  
Financial Performance ◽  
Performance Assessment ◽  
Rank Order ◽  
Multiple Criteria Decision Making ◽  
Data Envelopment ◽  
Step Size ◽  
Decision Making Unit ◽  
Common Weight

Economic and financial performance assessment possesses an important role for efficient usage of available resources. In this study, a novel common weight multiple criteria decision making (MCDM) approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs as well as multiple outputs. The robustness of the developed model, which provides a rank-order with enhanced discriminatory characteristics and improved weight dispersion, is illustrated by two case studies that aim to provide economic and financial performance assessment. The first study presents an evaluation of Morgan Stanley Capital International emerging markets, whereas the second case study ranks the Turkish deposit banks using the proposed methodology as well as providing a comparative evaluation with several other approaches addressed in earlier works. The results indicate that the introduced approach guarantees to identify the best performing DMU without including a discriminating parameter requiring an arbitrary step size value in model formulation while also achieving an improved weight dispersion for inputs and outputs.

scholarly journals Fuzzy Collaborative Clustering-Based Ranking Approach for Complex Objects

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Shihu Liu ◽  
Xiaozhou Chen ◽  
Tauqir Ahmed Moughal ◽  
Fusheng Yu
Keyword(s):  
Ranking Algorithm ◽  
Ranking Methods ◽  
Complex Objects ◽  
Ranking Problem ◽  
Relational Information ◽  
Individual Attribute ◽  
Collaborative Clustering ◽  
Attribute Information ◽  
Before And After ◽  
The Difference

This paper makes a discussion on the ranking problem of complex objects where each object is composed of some patterns described by individual attribute information as well as the relational information between patterns. This paper presents a fuzzy collaborative clustering-based ranking approach for this kind of ranking problem. In this approach, a referential object is employed to guide the ranking process. To achieve the final ranking result, fuzzy collaborative clustering is carried on the patterns in the referential object by using the collaborative information obtained from each ranked object. Since the collaborative information of ranking objects is represented by cluster centers and/or partition matrices, we give two forms of the proposed approach. With the aid of fuzzy collaborative clustering, the ranking results can be obtained by comparing the difference of the referential object before and after collaboration with respect to ranking objects. One can find that this proposed ranking approach is totally different from the previous ranking methods because of its completely collaborative clustering mechanism. Moreover, some synthetic examples show that our proposed ranking algorithm is valid.

scholarly journals Uniformization of frontiers in non-radial ZSG-DEA models: an application to airport revenues

2010 ◽  
Vol 30 (1) ◽  
pp. 175-193 ◽  
Author(s):  
Aline Bandeira de Mello Fonseca ◽  
João Carlos Correia Baptista Soares de Mello ◽  
Eliane Gonçalves Gomes ◽  
Lidia Angulo Meza
Keyword(s):  
Data Envelopment Analysis ◽  
Theoretical Approach ◽  
Approximate Algorithm ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Dea Models ◽  
Weights Restrictions ◽  
Zero Sum

We propose in this paper an extension to the Zero Sum Gains Data Envelopment Analysis model (ZSG-DEA). The proposed approach takes into account, simultaneously, non-radial projections and cone-ratio weights restrictions. We developed an iterative approximate algorithm to solve this model, as in the case study it is oriented only to the constant sum output. The theoretical approach is applied to the concession of discounts and surcharges problem, in terms of airport fees.

scholarly journals EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS

2004 ◽  
Vol 5 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Yossi Hadad ◽  
Lea Friedman ◽  
Aviad A. Israeli
Keyword(s):  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Ranking Method ◽  
New Method ◽  
Data Envelopment ◽  
Ranking Methods ◽  
Multiple Outputs ◽  
Highly Correlated ◽  
Using Data

This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

scholarly journals Modified Malmquist Productivity Index Based on Present Time Value of Money

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Farhad Hosseinzadeh Lotfi ◽  
Golamreza Jahanshahloo ◽  
Mohsen Vaez-Ghasemi ◽  
Zohreh Moghaddas
Keyword(s):  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Time Value Of Money ◽  
Index Model ◽  
Time Value ◽  
Dea Models ◽  
Value Of Money

Data envelopment analysis (DEA) models can calculate the Malmquist Productivity Index (MPI). Classic Malmquist Productivity Index shows regress and progress of a DMU in different periods with efficiency and technology variations without considering the present value of money. This issue is of major importance since while a currency of in previous year is not equal to that of now this would yield bias results which can affect the correct interpretation. The index developed here is defined in terms of Modified Malmquist Productivity Index model, which can calculate progress and regress by using the factor of present time value of money. The incorporation of present time value of money is also calculated within the framework of data envelopment analysis. This factor is fundamental and should be considered in DEA Malmquist Productivity Index. Moreover, here, differences between presented models are compared to those of previous ones indeed, biased results will be shown in the case study in banks, and problem and solution have been investigated in the literature.

scholarly journals Benchmarking with network dea in a fuzzy environment

2019 ◽  
Vol 53 (2) ◽  
pp. 687-703 ◽  
Author(s):  
Adel Hatami-Marbini
Keyword(s):  
Classification Scheme ◽  
Data Envelopment ◽  
Network Dea ◽  
Performance Benchmarking ◽  
Fuzzy Environment ◽  
Dea Models ◽  
Banking Education ◽  
Business Engineering ◽  
Fuzzy Network

Benchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has been practically and theoretically developed in distinct fields such as banking, education, health, and so on, benchmarking of supply chains with multiple echelons that include certain characteristics such as intermediate measure differs from other practices. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying the organizations. We finally provide a case study of the airport and travel sector to elucidate the details of the proposed method in this study.

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Izadikhah ◽  
Reza Farzipoor Saen ◽  
Kourosh Ahmadi ◽  
Mohadeseh Shamsi
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Algorithm ◽  
Data Envelopment ◽  
Screening System ◽  
Content Type ◽  
Efficient Frontiers ◽  
Dea Models ◽  
Clustering Approach ◽  
First Time

PurposeThe aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.Design/methodology/approachFirst, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.FindingsThis paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.Originality/valueThe main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

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