Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms characterised by fuzzy numbers in DEA to reflect the decision-makers' intuition and subjective judgements. This ...

data envelopment analysis (dea) is a method to evaluate the relative efficiency of decision making units (dmus). in this method, the issue has always been to determine a set of weights for each dmu which often caused many problems. since the dea models also have the multi-objective linear programming (molp) problems nature, a rational relationship can be established between molp and dea problem...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

in models of data envelopment analysis (dea), an optimal set of input and output weights is generally as-sumed to represent the assessed decision making unit (dmu) in the best light in comparison to all the other dmus. these sets of weights are, typically, different for each of the participating dmus. thus, it is important to find a common set of weights (csw) across the set of dmus. in this pa...

data envelopment analysis (dea) is a widely recognized approach for evaluating the efficiencies of decision making units (dmus). because of easy and successful application and case studies, dea has gained much attention and widespread use by business and academy researchers. the conventional dea models (e.g. bcc and ccr) make an assumption that input and output data are exact values on a ratio ...

envelopment analysis (dea) is a very eective method to evaluate the relative eciency of decision-making units (dmus). dea models divided all dmus in two categories: ecient and inecientdmus, and don't able to discriminant between ecient dmus. on the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ...

Generally, a typical problem which is crucial in a manufacturing system is increasing the production rate.  To cope with the problem, different types of techniques are used in companies by trial and error which imposes high costs on them. Using simulation as a tool for assessing the effect of alterations on the performance of the overall system might be significant. This paper considers a simul...