scholarly journals Ranking DMUs by Calculating the Interval Efficiency with a Common Set of Weights in DEA

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Vahideh Rezaie ◽  
Tahir Ahmad ◽  
Siti-Rahmah Awang ◽  
Masumeh Khanmohammadi ◽  
Normah Maan
Keyword(s):  
Decision Maker ◽  
Relative Efficiency ◽  
Efficiency Score ◽  
Upper Bounds ◽  
Data Envelopment ◽  
Lower And Upper Bounds ◽  
Common Set Of Weights ◽  
Overall Performance ◽  
Interval Efficiency ◽  
Decision Making Units

To evaluate the performance of decision making units (DMUs), data envelopment analysis (DEA) was introduced. Basically, the traditional DEA scheme calculates the best relative efficiency score (i.e., the “optimistic” efficiency) of each DMU with the most favorable weights. A decision maker may be unable to compare and fully rank the efficiencies of different DMUs that are calculated using these potentially distinct sets of weights on the same basis. Based on the literature, the assignable worst relative efficiency score (i.e., the “pessimistic” efficiency) for each DMU can also be determined. In this paper, the best and the worst relative efficiencies are considered simultaneously. To measure the overall performance of the DMUs, an integration of both the best and the worst relative efficiencies is considered in the form of an interval. The advantage of this efficiency interval is that it provides all of the possible efficiency values and an expanded overview to the decision maker. The proposed method determines the lower- and upper-bounds of the interval efficiency over a common set of weights. To demonstrate the implementation of the introduced method, a numerical example is provided.

scholarly journals An Interval Efficiency Measurement in DEA When considering Undesirable Outputs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Renbian Mo ◽  
Hongyun Huang ◽  
Liyang Yang
Keyword(s):  
Efficiency Score ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Mathematical Tool ◽  
Lower And Upper Bounds ◽  
Negative Data ◽  
Translation Invariant ◽  
Dea Models ◽  
Interval Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a popular mathematical tool for analyzing the relative efficiency of homogenous decision-making units (DMUs). However, the existing DEA models cannot tackle the newly confronted applications with imprecise and negative data as well as undesirable outputs simultaneously. Thus, we introduce undesirable outputs into modified slack-based measure (MSBM) model and propose an interval-modified slack-based measure (IMSBM) model, which extends the application of interval DEA (IDEA) in fields that concern with less undesirable outputs. The novelties of the model are that it considers the undesirable outputs while dealing with imprecise and negative data, and it is slack-based. Furthermore, the model with undesirable outputs is proven translation-invariant and unit-invariant. Moreover, a numerical example is provided to illustrate the changes of the lower and upper bounds of the efficiency score after considering the undesirable outputs. The empirical results show that, without considering undesirable outputs, most of the lower bounds of the efficiency scores will be overestimated when the DMUs are weakly efficient and inefficient. The upper bound will also change after considering undesirable outputs when the DMU is inefficient. Finally, an improved degree of preference approach is introduced to rank the DMUs.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

scholarly journals Assessing the relative efficiency of commercial banks in the Republic of North Macedonia: DEA window analysis

2020 ◽  
Vol 11 (2) ◽  
pp. 217-227
Author(s):  
Violeta Cvetkoska ◽  
Katarina Fotova Cikovic
Keyword(s):  
Commercial Banks ◽  
Relative Efficiency ◽  
Banking Sector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Window Analysis ◽  
The Republic ◽  
Dea Window Analysis ◽  
Decision Making Units ◽  
Selection Of

The aim of this paper is to assess the relative efficiency of commercial banks in one developing country, i.e. the Republic of North Macedonia by using the data envelopment analysis (DEA) technique-window analysis. The selection of inputs and outputs plays a key role when applying DEA for assessing the efficiency of decision-making units (DMUs). In the conducted research two inputs and two outputs have been selected. The sample consists of 14 commercial banks and the period that is being observed is an eleven year span from 2007 to 2017. According to the average efficiency score for the whole observed period, the most efficient bank belongs to the group of large banks, which simultaneously shows the highest efficiency. The banking sector in the Republic of North Macedonia, as a whole, showed the highest efficiency in 2007, and the lowest efficiency in 2011.

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

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

Mismanagement or Mismeasurement

2015 ◽  
pp. 276-322
Author(s):  
Abdel Latef Anouze ◽  
Ibrahim H. Osman
Keyword(s):  
General Framework ◽  
Evaluation Process ◽  
Future Research ◽  
Data Envelopment ◽  
Analytical Evaluation ◽  
Valuation Method ◽  
Multiple Input ◽  
Overall Performance ◽  
Five Phases ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known frontier valuation method to assess the performance of set of Decision Making Units (DMUs). It derives an overall performance for each DMU based on its efficiency relative to others. All DMUs use the same production function that transfers multiple-input into multiple-output of qualitative and quantitative values. Such big data necessitates the provision of a general framework to guide both researchers and practitioners in the analytical evaluation process for better insights. This chapter proposes a new roadmap to guide future research to implement rigorous and relevant DEA applications. This roadmap consists of five phases: Understand, Prepare, Analyze, Implement, and Monitor (AIM-UP). This roadmap could be used to evaluate the efficiency of resource utilization and the effectiveness of production by the operating processes. Finally, three case studies are used to illustrate DEA implementation, and an up-to-date review of DEA applications is conducted.

SIDE

2014 ◽  
Vol 5 (1) ◽  
pp. 39-58 ◽  
Author(s):  
Rashmi Malhotra
Keyword(s):  
Relative Efficiency ◽  
Search Space ◽  
Objective Measures ◽  
Data Envelopment ◽  
Multiple Sources ◽  
Swarm Optimization ◽  
Different Dimensions ◽  
Analytical Power ◽  
Decision Making Units ◽  
Analyze Data

To make sound decisions, managers analyze data from multiple sources using different dimensions and eventually integrate the results of their analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA) and particle swarm optimization (PSO), one of the major algorithms using swarm intelligence. DEA measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand PSO's main strength lies in exploring the entire search space. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies.

A Common Set of Weights for Ranking Decision-Making Units with Undesirable Outputs: A Double Frontiers Data Envelopment Analysis Approach

2018 ◽  
Vol 35 (06) ◽  
pp. 1850039 ◽  
Author(s):  
Lei Chen ◽  
Fei-Mei Wu ◽  
Feng Feng ◽  
Fujun Lai ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Optimal Solution ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Discrimination Power ◽  
Efficiency Optimization ◽  
Common Set Of Weights ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

Major drawbacks of the traditional data envelopment analysis (DEA) method include selecting optimal weights in a flexible manner, lacking adequate discrimination power for efficient decision-making units, and considering only desirable outputs. By introducing the concept of global efficiency optimization, this study proposed a double frontiers DEA approach with undesirable outputs to generate a common set of weights for evaluating all decision-making units from both the optimistic and pessimistic perspectives. For a unique optimal solution, compromise models for individual efficiency optimization were developed as a secondary goal. Finally, as an illustration, the models were applied to evaluate the energy efficiency of the Chinese regional economy. The results showed that the proposed approach could improve discrimination power and obtain a fair result in a case where both desirable and undesirable outputs exist.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

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