Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different mult...

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The main purpose of this paper ...

In data envelopment analysis (DEA) efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. By means of modified CCR model, in which the test DMU is excluded from the reference set, we are able to determine what perturbations of data can be tolerated before frontier DMUs become nonfrontier. In this paper we discuss simultaneous data perturbations i...

Data Envelopment Analysis (DEA) is one of the best methods for measuring the efficiency and productivity of Decision Making Units (DMU). Evaluating the efficiency of DMUs which have two or several stages by using the conventional DEA models, is equal to consider them as black box. This method, omits the effect of intermediate measure on efficiency. Therefore, just the first network inputs and t...

In wireless sensor networks data aggregation is a very important one and at the same time the aggregation should be energy efficient and a lesser amount of delay. To solve the problem we propose Delay Efficient Distributed Data Aggregation (DEDA) Scheduling Algorithm for Wireless Sensor Network (WSN) to handle the delay and energy tradeoff in the process of aggregation using the timeout concept...

Amixed integer linear model for selecting the best decisionmaking unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550–554], which involves many unnecessary constraints and requires ...

We provide an alternative framework for solving Data Envelopment Analysis (DEA) models which, in comparison with the standard Linear Programming (LP) based approach that solves one LP for each Decision Making Unit (DMU), delivers much more information. By projecting out all the variables which are common to all LP runs, we obtain a formula into which we can substitute the inputs and outputs of ...

It is well established that multiple reference sets may occur for a decision making unit (DMU) in the non-radial DEA (data envelopment analysis) setting. As our first contribution, we differentiate between three types of reference set. First, we introduce the notion of unary reference set (URS) corresponding to a given projection of an evaluated DMU. The URS includes efficient DMUs that are act...

Calibrating the dose per monitor unit (DMU) for individual patients is important to deliver the prescribed dose in radiation therapy. We have developed a DMU calculation method combining measurement data and calculation with a simplified Monte Carlo method for the double scattering system in proton beam therapy at the National Cancer Center Hospital East in Japan. The DMU calculation method det...