Data Envelopment Analysis, DEA
Amir Reza Bazargan; Seyyed Esmaeil Najafi; Farhad Hosseinzadeh Lotfi; Mohammad Fallah
Abstract
The data envelopment analysis method is commonly used to measure efficiency. An estimate of the relative efficiency of this model is derived by calculating the ratio between inputs and outputs. Data envelopment analysis models can also be applied to network structures due to the extension of these models. ...
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The data envelopment analysis method is commonly used to measure efficiency. An estimate of the relative efficiency of this model is derived by calculating the ratio between inputs and outputs. Data envelopment analysis models can also be applied to network structures due to the extension of these models. Supply chain management is a novel approach that governed production management in recent years. In complex and dynamic environments, the petrochemical industry requires an investigation system similar to those used by other organizations to inform about its activity's desirability, especially in complex and dynamic environments. This research focused on the petrochemical company supply chain. Laboratory studies, experts, and visits to petrochemical sites were used to identify production processes and determine indicators. After that, they were evaluated with an envelopment model and a coefficient corresponding to the identified petrochemical supply chain structure. The aggregate and componentwise efficiency of the studied units in petrochemical were also examined from 2016 to 2019.
Data Envelopment Analysis, DEA
reza rasi nojehdehi; hadi bagherzadeh valami
Abstract
Network DEA models deal with measurements of relative efficiency of Decision-Making Units when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and ...
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Network DEA models deal with measurements of relative efficiency of Decision-Making Units when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and input to another one. Therefore, improving the efficiency score of one division by increasing its output may reduce the score of another division because of increasing its input. To address this conflict, in the present paper we proposed a new approach in SBM framework which provides deeper insights regarding the sources of inefficiency. The proposed approach is a Two-Phase procedure in which Phase-I determine the role of intermediate measures by solving a linear program and partitions the intermediate measures into three groups of “input type”, “output type” and “fixed-flows” and Phase-II measures the scores of the DMUs under evaluation. Providing a classification for intermediate products and account their excesses or shortfalls in efficiency calculation while the continuity of link flows between subunits are kept, are the advantages of the proposed approach.
Data Envelopment Analysis, DEA
Sarvar Sadat Kassaei; Farhad Hosseinzadeh Lotfi; Alireza Amirteimoori; Mohsen Rostamy-Malkhalifeh; Bijan Rahmani
Abstract
Congestion is one of the important concepts in data envelopment analysis that occurs when excessive inputs reduce the maximally possible outputs. Identification and elimination of congestion have a significant impact on reducing inputs along with increasing outputs. Hence, various studies have been conducted ...
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Congestion is one of the important concepts in data envelopment analysis that occurs when excessive inputs reduce the maximally possible outputs. Identification and elimination of congestion have a significant impact on reducing inputs along with increasing outputs. Hence, various studies have been conducted to detect and evaluate congestion. However, in today's world, no organization can achieve its final output with just one process of input. In other words, today's organizations have a network structure that consists of several subsections. Ignoring the existing influences among the subsections processes may lead to inadequate or even incorrect results for evaluating the congestion. While all of the existing methods only evaluate the congestion of each subsection or the whole system independently. Therefore, in this paper, the concept of congestion is developed for a specific and so practical case of network structure called “two-stage network structure”. This case of network structure consists of two series stage such that stage 1 consume some primary inputs to produce some intermediate outputs. In the following, the intermediate outputs are used as the inputs of stage 2 to produce the final output. Here, the concept of congestion is defined for systems with a two-stage structure. Then, to examine the congestion of each stage as well as the congestion of the whole system, a single linear programming model is proposed. The validity of the proposed model is investigated using several theorems and it is shown that the new definition is a generalization of the previous definitions of the congestion for the black-box systems. Finally, the proposed model is applied to a case study including 24 non-life insurance companies in Taiwan.
Data Envelopment Analysis, DEA
Abbas Ghomashi Langroudi; Masomeh Abbasi
Abstract
The Cross-efficiency method in data envelopment analysis (DEA) has widely been used as a suitable utility for ranking decision-making units (DMUs). In this paper, for overcoming the issue of the existence of multiple optimal solutions in cross-efficiency evaluation, we use the neutral strategy to design ...
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The Cross-efficiency method in data envelopment analysis (DEA) has widely been used as a suitable utility for ranking decision-making units (DMUs). In this paper, for overcoming the issue of the existence of multiple optimal solutions in cross-efficiency evaluation, we use the neutral strategy to design a new secondary goal. Unlike the aggressive and benevolent formulations in cross-efficiency evaluation, the neutral cross-efficiency evaluation methods have been developed in a way that is only concerned with their own interests and is indifferent to other DMUs. The proposed secondary goal introduces a new cross-efficiency score by maximizing the sum of the output weights. The first model is then extended to a cross-weight evaluation, which seeks a common set of weights for all the DMUs. Finally, we give two numerical examples to illustrate the effectiveness of the proposed neutral models and the potential applications in ranking DMUs by comparing their solutions with those of alternative approaches.
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.
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.
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.
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.
