The full ranking or complete ranking of decision making units is one of the main ‎issues in data envelopment analysis. A full ranking is a ranking that considers all ‎efficient and inefficient units simultaneously and finds a ranking for them. Almost all ‎of More
The full ranking or complete ranking of decision making units is one of the main ‎issues in data envelopment analysis. A full ranking is a ranking that considers all ‎efficient and inefficient units simultaneously and finds a ranking for them. Almost all ‎of the developed ranking methods consider only the efficient units. On the other hand, ‎ranking inefficient units by traditional data envelope analysis models are also ‎inaccurate due to ignoring the role of slacks. In the present paper two novel methodologies are proposed in order to fully ranking of decision making units. In the presented ‎approach, all of the decision making units participate in a competition in a way that all ‎are projected onto the efficient frontier considering common weights. Then, according ‎to the profit that each unit attains to reach this standard level, a rank order of all ‎decision making units are obtained.In the first method , the satisfaction of units is measured in the competition and the satisfaction of the units that have the lowest satisfaction is improved. in the second method, by setting up the cross - profit table, the optimal weights of all units are taken into account in competition. After all, the proposed methods are applied on sample problems.
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Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful info More
Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful information to decision makers (DMUs) about optimal decision making activities. This concept defines the superiority of a unit in terms of efficiency and effectiveness on other units. The calculation of the efficiency of the units in the two-stage DEA network was performed, and the efficiency of the two-stage unit could be a suitable criterion for ranking one unit. But the main problem is the time when some efficient units all rank as one. So far, there is no linear model for solving this problem.The purpose of this research is to provide a model for ranking of effecient units using the common weight method in a two-stage DEA network.
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Data Envelopment Analysis (DEA) deals with evaluating a set of decision-making units (DMUs) based on nonparametric mathematical approaches. In classical DEA models, the given set of factors of DMUs is divided into two categories, inputs and outputs, while in some practi More
Data Envelopment Analysis (DEA) deals with evaluating a set of decision-making units (DMUs) based on nonparametric mathematical approaches. In classical DEA models, the given set of factors of DMUs is divided into two categories, inputs and outputs, while in some practical problems there are some measures whose membership to these categories is unclear. It means these factors treat different input or output roll for various DMUs. This kind of factors is known as a flexible measure. The proposed models for this purpose are DMU-oriented in which each DMU evaluate its efficiency and classify flexible measures for all other DMUs regards to its benefit. This classification may not be optimal for every DMUs. In this paper, we present a model based on a common set of weights (CSW). The advantage of the proposed model is to classify flexible measures uniquely for all DMUs. At the end of paper, the proposed model is applied in an example of wind power farms.
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تحلیل پوششی داده ها (DEA) روشی برای ارزیابی عملکرد مجموعه ای از واحدهای تصمیم گیرنده (DMU) متجانس با چندین ورودی و چندین خروجی می باشد. . مدل های مختلفی برای محاسبه کارایی در DEA وجود دارند که یکی از آن ها، مدل مجموعه وزن های مشترک (CSW) می باشد، که به طور گسترده ای توس More
تحلیل پوششی داده ها (DEA) روشی برای ارزیابی عملکرد مجموعه ای از واحدهای تصمیم گیرنده (DMU) متجانس با چندین ورودی و چندین خروجی می باشد. . مدل های مختلفی برای محاسبه کارایی در DEA وجود دارند که یکی از آن ها، مدل مجموعه وزن های مشترک (CSW) می باشد، که به طور گسترده ای توسط افراد و متخصصان DEA مورد استفاده قرار می گیرد. در تحلیل پوششی داده های کلاسیک تمام مقادیر ورودی ها و خروجی ها به طور دقیق مشخص می باشند. با این حال این فرض ممکن است در مسائل کاربردی همواره برقرار نباشد. یکی از روش های مهم برای مقابله با داده های نامشخص، بررسی داده های تصادفی در DEA است. این مقاله مدل مجموعه وزن های مشترک را برای حالتی که ورودی ها و خروجی ها تصادفی هستند، توسعه می دهد. سپس مدل تصادفی وزن مشترک (SCSW) به یک مدل قطعی غیر خطی تبدیل می گردد. پس از آن مدل قطعی به یک مدل برنامه ریزی درجه دوم تبدیل می شود. . کارایی به دست آمده با استفاده از داده های تصادفی را کارایی تصادفی می نامیم. مفهوم ارائه شده در این مقاله با مثال عددی که مربوط به شعبه های یک بانک ایرانی است نشان داده می شود.
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The traditional data envelopment analysis model allows the decision maker unit (DMU) to use the most favorable weights to evaluate the efficiency. This type of evaluation with the most flexibility can prevent the full ranking of DMUs and the evaluation results for DMUs More
The traditional data envelopment analysis model allows the decision maker unit (DMU) to use the most favorable weights to evaluate the efficiency. This type of evaluation with the most flexibility can prevent the full ranking of DMUs and the evaluation results for DMUs are unacceptable. To solve this problem in data envelopment analysis, a common weight approach based on the concept of the degree of satisfaction of a DMU has been introduced. Also, to solve a common weight model, which is a nonlinear model, an algorithm is presented that can be modeled step by step. In this paper, we will evaluate the performance of companies active in Tehran Stock Exchange using a common weight model for 24 companies active in Tehran Stock Exchange. The weighted average performance of Tehran Stock Exchange companies for the years 1394 and 1395. The results show that this method will rank companies without any interference during these two years.
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