data envelopment analysis (dea) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. it represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. the dea technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic co...

  Performance evaluation of universities is an important issue between researchers. Classic data envelopment analysis (DEA) models with deterministic data have been used by many authors to measure efficiency of universities in different countries. However, DEA with stochastic data are, rarely used to measure efficiency of universities. In this paper, input oriented model in stochastic data env...

The non-differentiability and implicit definition of boundary of production possibility set (PPS) in data envelopment analysis (DEA) are two important difficulties for obtaining directional characteristics, including different elasticity measures and marginal rates of substitution. Also, imposing weight restrictions in DEA models have some shortcomings and misunderstandings. In this paper we ut...

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 ...

Data envelopment analysis (DEA) with considering the best condition for each decision making unit (DMU) assesses the relative efficiency for it and divides a homogenous group of DMUs in to two categories: efficient and inefficient, but traditional DEA models can not rank efficient DMUs. Although some models were introduced for ranking efficient DMUs, Franklin Lio & Hsuan peng (2008), proposed a...