This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. In conventional DEA inputs and outputs data are precise. However, traffic and transportation take place in an uncertain environment and input and output data might be imprecise. This article proposes a possibility approach for sol...

This article compares two techniques: Data Envelopment Analysis (DEA) and Factor Analysis (FA) to aggregate multiple inputs and outputs in the evaluation of decision making units (DMU). Dِata envelopment analysis (DEA), a popular linear programming technique, is useful to rate comparatively operational efficiency of DMUs based on their deterministic or stochastic input–output data. Factor analys...

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. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...

In many data envelopment analysis (DEA) applications, the analyst always confronts the difficulty that the selected data set is not suitable to apply traditional DEA models for their poor discrimination. This paper presents an approach using Shannon’s entropy to improve the discrimination of traditional DEA models. In this approach, DEA efficiencies are first calculated for all possible variabl...

Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are continuous in the standard DEA model whereas th...

an integrative approach of data envelopment analysis (dea) and malmquist productivity indicator for performance analysis of projects in subsea r&d; center (srdc) is presented. it is noticeable that, an output based model with constant efficiency was used for data analysis. furthermore, since the dea approach is merely used for homogeneous units, it was assumed that all the projects according to...

Congestion is an essential concept in data envelopment analysis (DEA). It occurs when the decrease at least one decision-making unit (DMU) input increases output without affecting other inputs or outputs. Although various methods have discussed congestion determination static DEA, calculation dynamic DEA under inter-temporal dependence has not been considered literature. This paper deals with c...

data envelopment analysis (dea) is a method to measure relative efficiency of a set of decision-making units (dmus) which uses multiple inputs and produces multiple outputs. in the conventional dea, crisp inputs and outputs are fundamentally necessary. but the observed values of inputs and outputs in real-world problems are sometimes imprecise. thus, performance measurement often needs to be do...

In this paper, a novel approach has been proposed to rank police administration units on the basis of their effective enforcement of crime prevention measures using Data Envelopment Analysis (DEA) and Clustering. The proposed approach will offer an effective mechanism not only to rank police administration units but also provide an evaluation tool to monitor the implementation of crime preventi...