Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values of the input and output data in real-life problems are sometimes imprecise or vague. Previous method...

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have con...

Data Envelopment Analysis (DEA) is the methodology for evaluating the relative productive efficiency of decision making units (DMUs) that produce multiple-outputs using multiple-inputs. DEA was proposed for the first time in 1957 by Farrell; nonetheless, the wide usage of this method begun with its generalization and the linear programming formulation which is due to Charnes et al. (1978) (for ...

Data envelopment analysis (DEA) is a common technique in measuring the relative efficiency of a set of decision making units (DMUs) with multiple inputs and multiple outputs. ‎‎Standard DEA models are ‎‎quite limited models‎, ‎in the sense that they do not consider a DMU ‎‎at different times‎. ‎To resolve this problem‎, ‎DEA models with dynamic ‎‎structures have been proposed‎.‎In a recent pape...

Knowledge sharing as one of the most crucial processes in knowledge management, operates in a dynamic environment. Dedicated tools to measure its performance under such an environment are not found in the literature. This paper aims to fill this void by proposing a hybrid model based on Data Envelopment Analysis (DEA). Monte Carlo simulation is incorporated into the model to handle stochastic d...

This study utilizes the Data Envelopment Efficiency (DEA) model to assess input–output efficiency from two perspectives. First, not considering the distribution of interval data, we introduce an adjusted parameter to transform interval data to determination data. Second, by contrast, we take into account the distribution characteristics of interval data and test the DEA model with interval data...

The changing economic conditions have challenged many financial institutions to search for more efficient and effective ways to assess emerging markets. Data Envelopment Analysis (DEA) is a widely used mathematical programming technique that compares the inputs and outputs of a set of homogenous Decision Making Units (DMUs) by evaluating their relative efficiency. In the conventional DEA model,...

Abstract: In the conventional data envelopment analysis (DEA), all the data assumes the form of crisp numerical values. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Some researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA by constructing linear programming (LP) models with ...

In the evaluation of non-efficient units by Data Envelopment Analysis (DEA) referenced Decision Making Units (DMU’s) have an important role. Unfortunately DMU’s with extra ordinary output can lead to a monopoly in a reference set, the fact called abnormality due to the outliers' data. In this paper, we introduce a DEA model for evaluating DMU’s under this circumstance. The layer model can resul...

Abstract In this paper, we propose a bounded DEA based model to measure the overall risk of failure modes. In the proposed model risk is measured within the range of an interval, whose performance is definitely superior to any one. The risks, obtained from bounded DEA models, turn out to be all intervals and are referred to as interval risk, which combine the best and the worst relative risk in...