Data Envelopment Analyses
Mostafa Radsar; Aliyeh Kazemi; Mohammadreza Mehregan
Abstract
Purpose: It is important to consider the uncertainty in the data, and know how to deal with it when evaluating efficiency by using data envelopment analysis; since the presence of small deviations in the data can lead to significant changes in efficiency results. However, in the real world in many cases, ...
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Purpose: It is important to consider the uncertainty in the data, and know how to deal with it when evaluating efficiency by using data envelopment analysis; since the presence of small deviations in the data can lead to significant changes in efficiency results. However, in the real world in many cases, the data is uncertain. The purpose of this paper is to present a robust model of network data envelopment analysis in order to measure efficiency in the presence of uncertainty.Methodology: A new approach to evaluate efficiency for network data envelopment analysis is first proposed. The definitive method presented in this paper involves undesirable output and can be used for different structures in network data envelopment analysis. Next by extending, the proposed model for uncertain data a new robust network data envelopment analysis model is presented for three-stage networks with undesirable outputs.Findings: The proposed model is used to evaluate the electricity regions of Iran. These regions involve a three-step process with undesirable outputs in some stages. The results show that the proposed model achieves the efficiency of the steps and the total efficiency simultaneously. In addition, the overall network efficiency score can be a basis to rank the areas.Originality/Value: The proposed model is a new model in the field of efficiency evaluation in conditions of uncertainty and having an undesirable output.
Data Envelopment Analyses
Morteza Shafiee; Hilda Saleh; Ramin Ziyari
Abstract
Purpose: Despite increasing advances in project management, a very large proportion of projects still fail. Because there is no comprehensive approach to fully evaluate projects. Therefore, in this research, a new method for evaluating the efficiency of projects has been introduced, which is well understood ...
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Purpose: Despite increasing advances in project management, a very large proportion of projects still fail. Because there is no comprehensive approach to fully evaluate projects. Therefore, in this research, a new method for evaluating the efficiency of projects has been introduced, which is well understood due to the quantitative and qualitative nature of the designed model. Methodology: This research is applied in nature and the research method used in it is case study. Various methods such as library studies, searching articles and journals related to the research topic have been used to obtain information in the theoretical sections, and interviews and questionnaire have been used to determine the indicators. After extracting the indicators and designing the network structure based on the perspective of the balanced scorecard, the data envelopment analysis network model was designed and then the model was solved and finally the results were analyzed.Findings: Using the designed model, the efficiency and performance of Shiraz Civil Institute projects is evaluated from four financial perspectives, stakeholders and partners, and internal processes, and growth and learning. And according to the results, 33% of the projects of the Civil Institute are inefficient and the rest of the projects are efficient.Originality/Value: The model presented in this paper is a combination of network data envelopment analysis and balanced scorecard, and a systematic relationship has been established between the two methods, so that one of them complements and covers the weaknesses of the other method.
Data Envelopment Analyses
Naser Amani; Hadi Bagherzadeh valami
Abstract
Data Envelopment Analysis (DEA) is a method based on linear programming to measure the efficiency of Decision-Making Units (DMU). In classic models of DEA, the whole system had been usually considered as a Decision-Making Units to evaluate respective efficiency and it is also ignored the separate processes ...
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Data Envelopment Analysis (DEA) is a method based on linear programming to measure the efficiency of Decision-Making Units (DMU). In classic models of DEA, the whole system had been usually considered as a Decision-Making Units to evaluate respective efficiency and it is also ignored the separate processes inside the system. Whereas, the internal relations of various sectors of a Decision-Making Unit can have had diverse structures which cause complexity in evaluating its efficiency, because, the type of structures and the performance of these components would have different effects on efficiency of the system. Network standpoint is one of the appropriate ways for the internal relations of units’ modelling and the relation among sub-units in a DMU may be communicated in series, parallel or mixed way. In this paper, a new convert called Star Structure was introduced as a comprehensive one. The one that every structure existing between a Decision-Making Units’ sub-units can easily be converted to such structure so that can accurately evaluate a Decision-Making Units’ efficiency and also using star structure, we evaluated the performance of regional electronic companies in Iran.
Data Envelopment Analyses
Abbasali Noora; Faranak Hosseinzadeh Saljooghi; Maryam Khodadadi
Abstract
In the real world, there are decision-making units in which the production process can be considered as a two-stage or multi-stage process. In order to evaluate these types of units, the network data envelopment analysis method is used. In this paper, Two-stage units have been investigated, which in ...
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In the real world, there are decision-making units in which the production process can be considered as a two-stage or multi-stage process. In order to evaluate these types of units, the network data envelopment analysis method is used. In this paper, Two-stage units have been investigated, which in the two-stage process are the outputs of the first stage of the second stage inputs, which are referred to as "middle sizes".The purpose of this research is to determine the most effective scale of the production unit scale using a two-step process based on the demand level.In this regard, while determining the units of MPSS with ordinary DEA methods, we will generalize it in two-stage models.Then, the maximum and minimum amount of production, the production units that are in the most efficient scale of the scale, are obtained at each of the stages separately and then generalized for the whole process.We consider supply and demand as two output indicators and we determine the demand level for each step separately and then the whole process so that we can obtain the maximum and minimum amount of demand.