Decision-making based on expert’s opinion by data envelopment analysis (DEA) models requires crisp number. However, the assumption of expert might not always be accurate. This paper introduces fuzzy DEA credibility constrained and relative closeness (RC) index to cope with uncertainty data. The proposed approach transforms traditional events using measure. Furthermore, RC was used increase disc...

Data envelopment analysis (DEA) is a mathematical method to evaluate the performance of decision-making units (DMU). In the performance evaluation of an organization based on the classical theory of DEA, input and output data are assumed to be deterministic, while in the real world, the observed values of the inputs and outputs data are mainly fuzzy and random. A normal distribution is a contin...

In original Data Envelopment Analysis (DEA) models for measuring the relative efficiencies of a set of Decision Making Units (DMUs) using various inputs to produce various outputs are limited to crisp data. To deal with imprecise data, the notion of fuzziness has been introduced. this paper develops a procedure to measure the efficiencies of DMUs with fuzzy observations. The basic idea is to tr...

The economic, social, and ecological wellbeing of a society are all negatively impacted by the traffic congestion issue. Intelligent Transportation Systems (ITS) have been used to better sustainable transportation systems across globe, helping lessen unsustainable effects clogged highway goal may be reached examining positive ITS deployments had on urban areas in United States. In addition stat...

Data envelopment analysis (DEA) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (DMUs) where multiple inputs and outputs exist. In the conventional applications of DEA, the data are considered as specific numerical values with explicit designation of being an input or output. However, the observed values of the data are sometimes imprec...

in this paper it has been tried to present a model by usingsome changes in the classical dea models and combining these modelswith fuzzy models to eliminate these weaknesses. inputs and outputs arerecognized by using mcdm and ahp techniques and experts views. inthe next step, the gathered data is compounded by fuzzy techniques andentered in the special dea model after changing to the crisp amou...