Standard data envelopment analysis (DEA) supposes that measure status from point of view input or output is known. Nevertheless, in some situations, determining the status of a performance measure is not easy. Measures with unknown status of input /output are called flexible measures. Moreover, traditional DEA models do not deal with imprecise data and assume that all input and output are exact...

The purpose of this study is to measure and compare the efficiencies for 35 manufacturing firms listed in Amman Stock exchange (ASE) Jordan over period 2009-2017. A panel data was collected nine years, from annual reports firms. Data Envelopment Analysis (DEA) used an average efficiency score each firm, DEA also find a scores, since available short years bootstrap technique estimate confidence ...

It has been shown in prior work in management science, statistics and machine learning that using an ensemble of models often results in better performance than using a single ‘best’ model. This paper proposes a novel Data Envelopment Analysis (DEA) based approach to combine models. We prove that for the 2-class classification problems, DEA models identify the same convex hull as the popular RO...

Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist ”exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Also DEA requires that the data for all discretionary inp...

Chiou et al. (2010) (A joint measurement of efficiency and effectiveness for non-storable commodities: integrated data envelopment analysis approache s. European Journal of Operationa l Research 201, 477– 489) propose an integrated data envelopment analysis model in measuring decision making units (DMUs) that have a two-stage internal netwo rk structure with multiple inputs, outputs, and consum...

Missing values in inputs, outputs cannot be handled by the original data envelopment analysis (DEA) models. In this paper we introduce an approach based on interval DEA that allows the evaluation of the units with missing values along with the other units with available crisp data. The missing values are replaced by intervals in which the unknown values are likely to belong. The constant bounds...

Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. Because of the possible infeasibility of super-efficiency DEA model, the use of super-efficiency DEA model has been restricted to the situations where constant returns to scale (CRS) are assumed. It is shown that one of the input-oriented and ...

Traditional DEA models ignore the internal process of production systems and are not able to identify the cause of deficiency in efficiencies measuring. At this research, traditional DEA model and two-stage DEA model were used to measure the efficiency of Iranian private insurance companies during 2007-2009. The results indicated that the traditional DEA model is not suitable for such kind of n...

DEA is a non-parametric and linear programming based technique that attempts to maximize a decision making unit’s (DMUs) relative efficiency, expressed as a ratio of outputs to inputs, by comparing a particular unit’s efficiency with the performance of a group of similar DMUs that are delivering the same service. The traditional DEA models treat DMUs as black boxes whose internal structure is i...