scholarly journals Performance analysis in the presence of bounded, discrete and flexible measures_

2021 ◽  
Vol 31 (3) ◽  
Author(s):  
Sohrab Kordrostami ◽  
Monireh Jahani Sayyad Noveiri
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Highway Maintenance ◽  
Real World Applications ◽  
The Status ◽  
Dea Models ◽  
Discrete Values ◽  
Flexible Measure ◽  
The Individual ◽  
University Performance

In conventional data envelopment analysis (DEA) models, the relative efficiency of decision making units (DMUs) is evaluated while all measures with certain input and/or output status are considered as continuous data without upper and/or lower bounds. However, there are occasions in real-world applications that the efficiency of firms must be assessed while bounded elements, discrete values, and flexible measures are present. For this purpose, the current study proposes DEA-based approaches to estimate the relative efficiency of DMUs where bounded factors, integer values, and flexible measures exist. To illustrate it, radial models based on two aspects, individual and aggregate, are introduced to measure the performance of entities and to handle the status of the flexible measure such that there are bounded components and discrete data. Applications of approaches proposed in the areas of quality management, highway maintenance patrols, and university performance measurement are given to clarify the issue and to show their practicability. It was found that the introduced procedure can determine practical projection points for bounded measures and integer values (from the individual DMU viewpoint) and can classify flexible measures along with evaluation of DMUs relative efficiency.

scholarly journals Efficiency Bounds for Two-Stage Production Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao Shi
Keyword(s):  
Production Systems ◽  
Efficiency Score ◽  
Insurance Companies ◽  
Practical Case ◽  
Data Envelopment ◽  
Production Structure ◽  
Two Stage ◽  
Decision Making Unit ◽  
Real World Applications ◽  
Dea Models

Traditional data envelopment analysis (DEA) models find the most desirable weights for each decision-making unit (DMU) in order to estimate the highest efficiency score as possible. These efficiency scores are then used for ranking the DMUs. The main drawback is that the efficiency scores based on weights obtained from the standard DEA models ignore other feasible weights; this is due to the fact that DEA may have multiple solutions for each DMU. To overcome this problem, Salo and Punkka (2011) deemed each DMU as a “Black Box” and developed models to obtain the efficiency bounds for each DMU over sets of all its feasible weights. In many real world applications, there are DMUs that have a two-stage production system. In this paper, we extend the Salo and Punkka’s (2011) model to a more common and practical case considering the two-stage production structure. The proposed approach calculates each DMU’s efficiency bounds for the overall system as well as efficiency bounds for each subsystem/substage. An application for nonlife insurance companies has been discussed to illustrate the applicability of the proposed approach and show the usefulness of this method.

A review of the literature on DEA models under common set of weights

2020 ◽  
Vol 15 (4) ◽  
pp. 1277-1300
Author(s):  
Ignacio Contreras
Keyword(s):  
Future Research ◽  
Data Envelopment ◽  
Systematic Revision ◽  
Systematic Classification ◽  
Content Type ◽  
Dea Models ◽  
Common Set Of Weights ◽  
The Individual ◽  
Selection Of

Purpose Data envelopment analysis (DEA) is a mathematical method for the evaluation of the relative efficiency of a set of alternatives, which produces multiple outputs by consuming multiple inputs. Each unit is evaluated on the basis of the weighted output over the weighted input ratio with a free selection of weights and is allowed to select its own weighting scheme for both inputs and outputs so that the individual evaluation is optimized. However, several situations can be found in which the variability between weighting profiles is unsuitable. In those cases, it seems more appropriate to consider a common vector of weights. The purpose of this paper is to include a systematic revision of the existing literature regarding the procedures to determine a common set of weights (CSW) in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure. The discussion and findings of this paper provide insights into future research on the topic. Design/methodology/approach This paper includes a systematic revision of the existing literature about the procedures to determine a CSW in the DEA context. The contributions are classified with respect to the methodology and to the main aim of the procedure. Findings The discussion and findings of the literature review might insights into future research on the topic. Originality/value This papers revise the state of the art on the topic of models with CSW in DEA methodology and propose a systematic classification of the contributions with respect to several criteria. The paper would be useful for both theoretical and practical future research on the topic.

scholarly journals A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xinna Mao ◽  
Zhao Guoxi ◽  
Mohammad Fallah ◽  
S. A. Edalatpanah
Keyword(s):  
Data Envelopment Analysis ◽  
Manufacturing System ◽  
Aggregation Operator ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Simple Construction ◽  
Mathematical Methods ◽  
Reliable Measurement ◽  
Real World Applications ◽  
Dea Models

Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may additionally be present in the manufacturing system, which wishes to be minimized. To tackle the mentioned issues and in order to obtain a reliable measurement that keeps original advantage of DEA and considers the influence of undesirable factors under the indeterminate environments, this paper presents a neutrosophic DEA model with undesirable outputs. The recommended technique is based on the aggregation operator and has a simple construction. Finally, an example is given to illustrate the new model and ranking approach in details.

scholarly journals Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis

2021 ◽  
Vol 10 (3) ◽  
pp. 301-310
Author(s):  
Nahia Mourad ◽  
Ahmed Mohamed Habib ◽  
Assem Tharwat
Keyword(s):  
Relative Efficiency ◽  
Statistical Tests ◽  
Internal Validity ◽  
Healthcare Systems ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Medical Practitioners ◽  
Tobit Analysis ◽  
Dea Models ◽  
Tobit Regression Analysis

The healthcare system is a vital element for any community, as it extremely affects the socio-economic development of any country. The current study aims to assess the performance of the healthcare systems of the countries above fifty million citizens in facing the spread of the COVID-19 pandemic since late December 2019. For this purpose, seven scenarios were adopted via the DEA methodology with six variables, which are the number of medical practitioners (doctors and nurses), hospital beds, Conducted Covid-19 tests, affected cases, recovered cases, and death cases. To shed light on the relative efficiency of drivers, the Tobit analysis was used. Besides, the study carried out various statistical tests for the DEA models' findings to validate the choice of the variables and the obtained scores. The DEA results reveal that less than half of the considered countries are relatively efficient. Moreover, the Tobit regression analysis showed that the main impact on the efficiency scores was due to the number of affected and recovered cases. Finally, the results of the tests of Spearman, Mann-Whitney U, and Kruskal-Wallis H indicate the internal validity and robustness of the chosen DEA models. The current study findings raise important implications, which can be helpful for decision makers regarding continuous improvement of performance, in which the findings assert the importance of achieving the best practices regarding relative efficiency through the linkage between the healthcare systems’ resources, and the needed outputs.

scholarly journals DEA-BASED INTEGRATED RELATIONSHIP OF RETURNS TO SCALE – AN APPLICATION TO ROAD MAINTENANCE IN TAIWAN

2012 ◽  
Vol 18 (5) ◽  
pp. 709-723 ◽  
Author(s):  
Gang Lee ◽  
Ming-Miin Yu ◽  
Lung-Chuang Wang
Keyword(s):  
Returns To Scale ◽  
Data Envelopment ◽  
Road Maintenance ◽  
Highway Maintenance ◽  
Multi Stage ◽  
The Status ◽  
Directorate General ◽  
Relationship Of ◽  
Constant Returns To Scale ◽  
Decision Making Units

An Integrated Relationship of Returns to Scale (IRRS) associated with multiple-stage Data Envelopment Analysis (DEA) is proposed for identifying the returns to scale (RTS) among decision-making units (DMUs) appropriately and accurately. The validity and feasibility of the proposed method is tested by using 31 case studies on highway maintenance and construction offices based on the data provided by the Directorate General of Highways (Taiwan). The results show that the multi-stage DEA method with IRRS is more useful than the traditional single-stage DEA for evaluating the status of RTS for each DMU. Among the 31 units evaluated, 14 units are categorized as having increasing returns to scale, 4 have decreasing returns to scale, and no unit has constant returns to scale; the returns for the remaining 13 units cannot be determined.

Measuring Relative Efficiency and Effectiveness

2014 ◽  
pp. 1529-1538 ◽  
Author(s):  
David Lengacher ◽  
Craig Cammarata ◽  
Shannon Lloyd
Keyword(s):  
Relative Efficiency ◽  
A Priori ◽  
Measurement Tool ◽  
Data Envelopment ◽  
Objective Data ◽  
Military Operations ◽  
Decision Sciences ◽  
University Courses ◽  
Efficiency And Effectiveness ◽  
Dea Models

Data Envelopment Analysis (DEA) has been used to supply decision makers and analysts with new insights into the efficiency of peer entities called decision making units (DMUs). The advantage of DEA is that it provides an objective data-driven assessment of performance, free of user bias. However, because factor weights are determined by an algorithm and not a priori, many researchers and practitioners have difficulty understanding DEA models and the scores they produce. This may explain why DEA is seldom covered in university courses in the decision sciences. The result of this lack of awareness and understanding is that DEA is underutilized as a performance measurement tool in commercial, government, and military operations. This chapter aims to address this issue by providing a lucid overview of DEA, replete with examples and suggestions to make DEA more accessible for researchers and practitioners alike. Additionally, our didactic approach includes step-by-step instructions for preparing data, choosing DEA models, and avoiding pitfalls.

Developing and training artificial neural networks using bootstrap data envelopment analysis for best performance modeling of sawmills in Ontario

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shashi K. Shahi ◽  
Mohamed Dia ◽  
Peizhi Yan ◽  
Salimur Choudhury
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Performance Modeling ◽  
Business Environment ◽  
Optimal Combination ◽  
Data Envelopment ◽  
Content Type ◽  
Dea Models ◽  
Ann Models ◽  
Artificial Neural

Purpose The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power. Design/methodology/approach The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill. Findings The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results. Originality/value The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

A LINEAR BILEVEL PROGRAMMING PROBLEM FOR OBTAINING THE CLOSEST TARGETS AND MINIMUM DISTANCE OF A UNIT FROM THE STRONG EFFICIENT FRONTIER

2012 ◽  
Vol 29 (02) ◽  
pp. 1250011 ◽  
Author(s):  
G. R. JAHANSHAHLOO ◽  
J. VAKILI ◽  
M. ZAREPISHEH
Keyword(s):  
Programming Problem ◽  
Bilevel Programming ◽  
Relative Efficiency ◽  
Minimum Distance ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
Bilevel Programming Problem ◽  
Linear Bilevel Programming ◽  
Distant Target ◽  
Dea Models

Data envelopment analysis (DEA) can be used for assessing the relative efficiency of a number of operating units, finding, for each unit, a target operating point lying on the strong efficient frontier. Most DEA models project an inefficient unit onto a most distant target, which makes its attainment more difficult. In this paper, a linear bilevel programming problem for obtaining the closest targets and minimum distance of a unit from the strong efficient frontier by different norms is provided. The idea behind this approach is that closer targets determine less demanding levels of operation for the inputs and outputs of the units to perform efficiently. Finally, it will be shown that the proposed method is an extension of the existing methods.

Introduction to Data Envelopment Analysis and its Applications

2015 ◽  
pp. 235-255 ◽  
Author(s):  
Ali Emrouznejad ◽  
Emilyn Cabanda
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Software Package ◽  
Theoretical Foundation ◽  
Data Envelopment ◽  
Dea Models ◽  
Dea Method

This chapter provides the theoretical foundation and background on Data Envelopment Analysis (DEA) method and some variants of basic DEA models and applications to various sectors. Some illustrative examples, helpful resources on DEA, including DEA software package, are also presented in this chapter. DEA is useful for measuring relative efficiency for variety of institutions and has its own merits and limitations. This chapter concludes that DEA results should be interpreted with much caution to avoid giving wrong signals and providing inappropriate recommendations.

scholarly journals Undesirable Outputs’ Presence in Centralized Resource Allocation Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Ghasem Tohidi ◽  
Hamed Taherzadeh ◽  
Sara Hajiha
Keyword(s):  
Resource Allocation ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Numerical Illustration ◽  
Allocation Model ◽  
Proposed Model ◽  
Dea Models ◽  
The Individual ◽  
Desirable And Undesirable Outputs ◽  
Theoretical Foundations

Data envelopment analysis (DEA) is a common nonparametric technique to measure the relative efficiency scores of the individual homogenous decision making units (DMUs). One aspect of the DEA literature has recently been introduced as a centralized resource allocation (CRA) which aims at optimizing the combined resource consumption by all DMUs in an organization rather than considering the consumption individually through DMUs. Conventional DEA models and CRA model have been basically formulated on desirable inputs and outputs. The objective of this paper is to present new CRA models to assess the overall efficiency of a system consisting of DMUs by using directional distance function when DMUs produce desirable and undesirable outputs. This paper initially reviewed a couple of DEA approaches for measuring the efficiency scores of DMUs when some outputs are undesirable. Then, based upon these theoretical foundations, we develop the CRA model when undesirable outputs are considered in the evaluation. Finally, we apply a short numerical illustration to show how our proposed model can be applied.

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