Data Envelopment Analysis, DEA
Abbasali Monzeli; Behrouz Daneshian; Gasem Tohidi; Shabnam Razavian; Masud Sanei
Abstract
We extend the concept of returns to scale in Data Envelopment Analysis (DEA) to the weight restriction environments. By adding weight restrictions, the status of returns to scale, i.e. increasing, constant, and decreasing, may need a change. We first define "returns to scale" underweight restrictions ...
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We extend the concept of returns to scale in Data Envelopment Analysis (DEA) to the weight restriction environments. By adding weight restrictions, the status of returns to scale, i.e. increasing, constant, and decreasing, may need a change. We first define "returns to scale" underweight restrictions and propose a method for identifying the status of returns to scale. Then, we demonstrated that this addition would usually narrow the region of the most productive scale size (MPSS). Finally, for an inefficient decision-making unit (DMU), we will present a simple rule for determining the status of returns to the scale of its projected DMU. Here, we carry out an empirical study to compare the proposed method's results with the BCC model. In addition, we demonstrate the change in the MPSS for both models. We have presented different models of DEA to determine returns to scale. Here, we suggested a model that determines the whole status to scale in decision-making units.Different models of DEA to determine returns to scale are presented. Here, we suggested a model that determines the whole status to scale in decision-making units.
Engineering Optimization
Sh. Ghasemi; A. Aghsami; M. Rabbani
Abstract
Data Envelopment Analysis (DEA) is one of the non-parametric methods for evaluating each unit's efficiency. Limited resources in the healthcare system are the main reason for measuring the efficiency of hospitals. Because Operating Rooms (OR) are the most vital part of any hospital, we determine the ...
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Data Envelopment Analysis (DEA) is one of the non-parametric methods for evaluating each unit's efficiency. Limited resources in the healthcare system are the main reason for measuring the efficiency of hospitals. Because Operating Rooms (OR) are the most vital part of any hospital, we determine the factors affecting operating rooms' efficiency and evaluate the performance and ranking of operating rooms in 10 of Tehran's largest hospitals. This model's inputs include accuracy in scheduling surgeries, average turnover time, number of successful surgeries and live patients, number of canceled surgeries, number of surgical errors, and number of emergency surgery. Also, outputs consist of the number of operating rooms and equipment, the average number of beds, the number of employees, and the patient satisfaction rate. First, we determine the weight of inputs and outputs by Group Analytic Hierarchy Process (GAHP) with considering experts' ideas in 10 hospitals; then, we utilize three types of DEA model which are input-oriented CCR (CCR-I), output-oriented CCR (CCR-O), input-output oriented CCR (CCR_IO) and AP models to estimate the efficiency of ORs and rank them.
Data Envelopment Analysis, DEA
F. Z. Montazeri
Abstract
One of the best techniques for evaluating the performance of organizations is data envelopment analysis. Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the performance of decision-making units (DMUs) that recognizes the relative performance of DMUs based on mathematical programming. ...
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One of the best techniques for evaluating the performance of organizations is data envelopment analysis. Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the performance of decision-making units (DMUs) that recognizes the relative performance of DMUs based on mathematical programming. The classic DEA model was initially formulated for optimal inputs and outputs, But in real-world problems, the values observed from input and output data are often ambiguous and random. In fact, decision-makers may be faced with a specific hybrid environment where there are fuzziness and randomness in the problem. To overcome this problem, data envelopment analysis models in the random fuzzy environment have been proposed. Although the DEA has many advantages, one of the disadvantages of this method is that the classic DEA does not actually give us a definitive conclusion and does not allow random changes in input and output. In this research data envelopment analysis models in fuzzy random environments is reviewed.
Fuzzy optimization
S. H. Mirzaei; A. Salehi
Abstract
In many real applications, the data of production processes cannot be precisely measured. Hence the input and output of Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) may be imprecise or fuzzy-numbered. In original DEA models, inputs and outputs are measured by exact values on a ratio ...
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In many real applications, the data of production processes cannot be precisely measured. Hence the input and output of Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) may be imprecise or fuzzy-numbered. In original DEA models, inputs and outputs are measured by exact values on a ratio scale, therefore conventional DEA can't easily measure the performance of DMUs and rank them. The researchers have introduced mane deferent model for ranking DMUs by fuzzy number. In this paper, we proposed a new method by using the Tchebycheff norm for ranking DMUs with fuzzy data. We explain our method by numerical example with the triangular fuzzy number.
Data Envelopment Analysis, DEA
M. V. Sebt; M. N. Juybari; V. R. Soleymanfar
Abstract
According to the increasing need of industry to financiers and investors in order to incorporate and encouraging them to invest in various fields of industry, the necessity of a method to help investors for making a decision has emphasized. We present a method which tries to omit to apply personally ...
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According to the increasing need of industry to financiers and investors in order to incorporate and encouraging them to invest in various fields of industry, the necessity of a method to help investors for making a decision has emphasized. We present a method which tries to omit to apply personally partial views in making a decision in order to make the results more reliable. This paper focuses on ranking investing opportunities. The strategy which has used is the Data Envelopment Analysis that the 4 main sub-models including Charnes, Cooper & Rhodes (CCR) model, Input Oriented Banker, Charnes & Cooper (BCC) model, Output Oriented BCC model, and Additive model have been utilized. The contribution of this research is using 4 DEA models for ranking projects in terms of feasibility whereas in the similar researches, as what found in the literature, the mentioned models have not been taken into account simultaneously. The developed model is applied in an Iranian investment company which has 15 investment opportunities that we have evaluated and ranked them based on 5 financial indices with DEA mechanism. Our approach can be performed by any investment company or financier to rank their investment projects considering feasibility study results of each investment opportunity.
Supply chain management
S. Tunali; G. Oztuzcu
Abstract
Effective design and management of Supply Chain Networks (SCN) support the production and delivery of products at low cost, high quality, high variety, and short lead times. In this study, a SCN is designed for an automotive company by integrating various approaches. The study has been carried out in ...
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Effective design and management of Supply Chain Networks (SCN) support the production and delivery of products at low cost, high quality, high variety, and short lead times. In this study, a SCN is designed for an automotive company by integrating various approaches. The study has been carried out in two phases: The first phase involves selecting suppliers and distributors by using Data Envelopment Analysis (DEA) and integer-programming model. In the second phase, first the priority ranking of selected suppliers and distributors is determined using the Analytical Hierarchy Process (AHP) and then these priority rankings are integrated into the transportation models developed to identify the optimal routing decisions for all members of the supply chain.
Data Envelopment Analysis, DEA
Z. Taeb; F. Hosseinzadeh Lotfi; S. Abbasbandy
Abstract
In the last years, several techniques have been reported for managing a system and recognition of the related decision making units. One of them is based on mathematical modeling. Efficiency of any system is very important for all decision makings. Often applied data have time dependent inputs/ outputs. ...
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In the last years, several techniques have been reported for managing a system and recognition of the related decision making units. One of them is based on mathematical modeling. Efficiency of any system is very important for all decision makings. Often applied data have time dependent inputs/ outputs. To calculate the efficiency of time dependent data, a new calculation method has been developed and reported here. By this method, the efficiency has been calculated, with minimum errors and minimum mathematical solving model. The data are often time dependent, therefore Spline function has been estimated as a function of time, without using any particular time. Based on this developed function, the efficiency of time dependent data of a numerical example has been calculated and reported.
Fuzzy optimization
M. M. Tavakoli; B. Molavi; H. Shirouyehzad
Abstract
In recent years, researchers in their studies considered human capital as one of the most important capitals of every organization and even some of them placed it beyond this definition and introduced it as the unique factor of creating the competitive advantage in the organization. Due to the importance ...
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In recent years, researchers in their studies considered human capital as one of the most important capitals of every organization and even some of them placed it beyond this definition and introduced it as the unique factor of creating the competitive advantage in the organization. Due to the importance of human capital management, by evaluating the performance of human capital management system, managers can be aware of their organization’s status from the perspective of human capital management creation and perform corrective practices better. In this study, a method for the performance evaluation and ranking of organizational unit is presented using fuzzy DEA. Therefore in the beginning, the performance of organizational units was evaluated using fuzzy DEA and then with the use of sensitivity analysis, the most effective criteria on the efficiency of organizational units were determined. Then using the efficiency of organizational units in the best and the worst states, ranking of organizational units has been paid. Finally to examine the functionality of the proposed method, Foolad Technic Company has been chosen as a case study and the procedure has been implemented in this company.
Data Envelopment Analysis, DEA
M. Nayebi; F Hosseinzadeh Lotfi
Abstract
The science of Data Envelopment Analysis (DEA) evaluates the effectiveness of decision making units. But, one of the problems of Data Envelopment Analysis (DEA) is that, if the number of units with the same efficiency equal to one was more than one, then we couldn’t select the best between them. ...
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The science of Data Envelopment Analysis (DEA) evaluates the effectiveness of decision making units. But, one of the problems of Data Envelopment Analysis (DEA) is that, if the number of units with the same efficiency equal to one was more than one, then we couldn’t select the best between them. It means that, we can’t rank them. Therefore, the need for ranking these units is considered by the managers. Different methods were proposed in this context. Most of these methods are modeled by DEA models. Due to the variety of ranking methods in DEA, this paper will describe ranking methods which are based on super-efficiency. More precisely, we introduced methods that rank using elimination (removing) of decision making units under the evaluation of observations(set). These methods have some advantages and disadvantages such as, model feasibility or infeasibility, stability or instability, being linear or nonlinear, being radial or non-radial, existence or non- existence of bounded optimal solution in objective function, existence or non- existence of multiple optimal solution, non-extreme efficient units ranking, complexity or simplicity of computational processes, that in this paper, Super Efficiency methods are compared with these eight properties.
Data Envelopment Analysis, DEA
F Hosseinzadeh Lotfi; M. Jahanbakhsh
Abstract
By distinction between efficiency and effectiveness scales, the aim of this paper is to propose a model that can show the differents of efficiency and effectiveness. For this purpose, enveloping form of ICCR model ,has considered to calculate simultaneously the influences of efficiency and effectiveness. ...
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By distinction between efficiency and effectiveness scales, the aim of this paper is to propose a model that can show the differents of efficiency and effectiveness. For this purpose, enveloping form of ICCR model ,has considered to calculate simultaneously the influences of efficiency and effectiveness. this model, is a linear programming model based on Data envelopment analysis (DEA), that combine the input and output oriented CCR model to investigate the efficiency and effectiveness impressed each other ,in a three-stage process. By applying the model on data of 24 bank branches, the result clarify comprehensive view of the performance of the branches that have been substantially three-stage.
S. Khoshfetrat; F. Hosseinzadeh-Lotfi
Volume 3, Issue 4 , December 2014, , Pages 13-20
Abstract
The data envelopment analysis (DEA) is a mathematical programming technique, which is used for evaluating relative efficiency of decision making units (DMUs). However, the DEA does not provide more information about the efficient DMUs. Recently, some researchers have been carried out in the background ...
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The data envelopment analysis (DEA) is a mathematical programming technique, which is used for evaluating relative efficiency of decision making units (DMUs). However, the DEA does not provide more information about the efficient DMUs. Recently, some researchers have been carried out in the background of using DEA models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). In this paper, an application of a common set of weights is used for determining priorities in the AHP. First, we determine DEA efficient alternatives as DMUs. Then, these alternatives are ranked according to the efficiency score weighted by the common set of weights in the AHP. This application is applied successfully and the result is valid and assured. A numerical example is utilized to illustrate the capability of this procedure.
A. Pakzad; A. Naderi
Volume 3, Issue 4 , December 2014, , Pages 21-40
Abstract
One of the major problems in organizations is resource limitation, so the main goal of each union is to maximize the usage of their recourses and improve its efficiency. Universities as a main body of each countries educational system have an important role in developing a country. Therefore, assessing ...
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One of the major problems in organizations is resource limitation, so the main goal of each union is to maximize the usage of their recourses and improve its efficiency. Universities as a main body of each countries educational system have an important role in developing a country. Therefore, assessing the efficiency of universities and improving the quality of them are important goals. The method of the data envelopment analysis (DEA) can rank the efficiency while the number of indicators does not exceed the specific amount. But in measurement of the universities’ efficiency, main intention is toward considering a comprehensive set of indicators and in this case discrimination power of the DEA method decreases and its results are unacceptable. In this study, we present a combined model of the joint multiple layer DEA (MLDEA) model and weight restrictions method in order to try to eliminate the weakness of the mentioned method. Educational units of ShahidBahonar University of Kerman are evaluated and ranked as a case study. Empirical results shows the efficiency of presented model based on discrimination power, weight allocating and possibility of implementing this model in evaluating the function of activities, which have many indicators along with hierarchical structure.
M. Rabbani; H. Yousefnejad; H. Rafiei
Volume 3, Issue 1 , May 2014, , Pages 49-54
Abstract
This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent ...
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This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent years, but locating CODP along a supply chain has not yet been completely investigated. Here we present a novel combined DEA/AHP method to tackle the problem of positioning CODP in a supply chain. Then in order to prove the applicability of the proposed structure in a real case, the model is implemented in a food processing supply chain.
F. Piran; F Hosseinzadeh Lotfi; M. Rostami-Malkhalifeh
Volume 2, Issue 4 , December 2013, , Pages 15-25
Abstract
Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This thesis introduces ...
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Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This thesis introduces the most productive scale size (MPSS), and anti- most productive scale size (AMPSS), and proposes several models to calculate various distances between DMUs and both frontiers. Specifically, the distances considered in this paper include: (1) both the distance to MPSS and the distance to AMPSS, where the former reveals a unit’s potential opportunity to become a best performer while the latter reveals its potential risk to become a worst performer, and (2) both the closest distance and the farthest distance to frontiers, which may proved different valuable benchmarking information for units. Subsequently, based on these distances, eight efficiency indices are introduced to rank DMUs. Due to different distances adopted in these indices, the efficiency of units can be evaluated from diverse perspectives with different indices employed. In addition, all units can be fully ranked by these indices.
F. Hosseinzadeh Lotfi; M. Rostamy mal khalifeh; M. Heydari Alvar
Volume 1, Issue 1 , June 2012, , Pages 1-9
Abstract
In this paper, a method for ranking efficient DMUs based on TOPSIS has been proposed. The difference between the distance of the center of gravity of all efficient DMUs to the ideal point and the anti-ideal point after and before deleting efficient DMUs one by one is the criteria of ranking efficient ...
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In this paper, a method for ranking efficient DMUs based on TOPSIS has been proposed. The difference between the distance of the center of gravity of all efficient DMUs to the ideal point and the anti-ideal point after and before deleting efficient DMUs one by one is the criteria of ranking efficient DMUs. In this paper, the proposed method is compared with AP (input oriented), MAJ (input oriented), AP(output oriented), MAJ (output oriented) models and norm l1 method. This comparison shows that the proposed method is better than the above-mentioned models. The proposed method is also always feasible and simpler in comparison with other methods.
M. Fallah; S.E. Najafi
Volume 1, Issue 1 , June 2012, , Pages 10-18
Abstract
Evaluating the performance of the different departments in an organization or evaluating and comparing the performance of a single department over different time points may boost the organizational efficiency and the involved industry. Making the most efficient use of the existing resources to realize ...
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Evaluating the performance of the different departments in an organization or evaluating and comparing the performance of a single department over different time points may boost the organizational efficiency and the involved industry. Making the most efficient use of the existing resources to realize all the expected outcomes. Therefore, in their projected plans, the efficiency enhancement specialists have always attempted to measure the existing efficiency of an organization and estimate the room for improvement. A Data Envelopment Analysis (DEA) approach would be an appropriate model to measure efficiency and provide answers to the above questions. In order to estimate the efficiency of a research unit over a period of years, first different indicators were identified and then through a questionnaire their priorities were determined. Finally, using the CCR model the performance efficiency and the ranking of the research center over the years were conducted.
H. Bagherzadeh Valami; S.E. Najafi; B. Farajollahzadeh
Volume 1, Issue 1 , June 2012, , Pages 19-28
Abstract
Data envelopment analysis (DEA) is a methodology for identifying efficient frontier of decision making units (DMUs) with multiple outputs and inputs. Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents ...
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Data envelopment analysis (DEA) is a methodology for identifying efficient frontier of decision making units (DMUs) with multiple outputs and inputs. Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. Context-dependent DEA measures the attractiveness and the progress for each DMU. Current paper extends the context-dependent DEA by ranking all units on the basis of attractiveness and progress measures. The method is applied to measure the attractiveness and progress of 49 bank branches, and ranking them with Context-dependent DEA.
