This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this l...

Data envelopment analysis (DEA) creates many opportunities for collaboration between analyst and decision-maker. There are, however, situations in which all of the decision-making units (DMUs) fall under the umbrella of a centralized decision maker that oversees them. Many organizations such as bank branches, chain stores, … can do this. This centralized decision maker unit expect that resource...

Data Envelopment Analysis (DEA) technique now widely use for efficiency evaluation of a set of Decision Making Units (DMUs). As regards of the necessity for ranking efficient units different DEA models presented each of which has advantages and rank efficient units from special aspects. Note that all the existing ranking models have disadvantages, as well and there is not a model in which all t...

Traditional data envelopment analysis (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multiple-inputs consumed to produce multiple-outputs. One of the drawbacks of these models is neglecting internal processes of each system, which may have intermediate products and/or independent inputs and/or outputs. In this paper some methods which are usab...

measuring the performance of a production system has been an important task in management for purposes of control, planning, etc. lord kelvin said :“when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind.” hence, manageme...

one of the major problems in data envelopment analysis (dea) is to determine the projection of inefficient decision making units (dmus) into the efficient frontier. in conventional dea models, inputs and outputs of inefficient dmus alter arbitrarily for reaching to the efficient frontier. nevertheless, sometimes the ability of dmus is defined and restricted. moreover, there are situations in th...

Although DEA is a powerful method in evaluating DMUs, it does have some limitations. One of the limitations of this method is the result of the evaluation is based on previously data and the results are not proper for forecasting the future changes. So For this purpose, we design feedback loops for forecasting inputs and outputs through system dynamics and simulation. Then we use DEA model to f...

Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple inputs and multiple outputs. DEA-based Malmquist productivity index measures the productivity change over time. We propose a dynamic DEA model involving network structure in each period within the framework a DEA. We have previously published the network DEA (NDEA) and the dynamic DEA ...

sohrabi and nalchigar (2010) proposed a new data envelopment analysis (dea) model to identify the most efficient decision-making unit (dmu) in presence of imprecise data. in this paper, it is shown that the proposed model is not able to determine the most efficient dmu and is randomly introduced an efficient dmu. in addition, it is shown that this model determines the most efficient dmu in the ...

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 envelop...