b. daneshian

Data envelopment analysis (DEA) assigns a score to each unit of the decision-making units being analyzed indicating the efficiency or inefficiency of that unit over the other units. However, in the early DEA models, there is no strategy to improve the efficiency of the efficient units. Therefore, in Paradi & Sowlati's (2004) practical boundary theory, they tried to expand these models to increase the efficiency of the efficient decision-making units. They had a basis for improving performance to a certain extent, thus, they presented the P-DEA linear programming model to extend the efficiency of the efficient units. Because of the staff management in organizations, it is important to increase the efficiency units in order to improve the organization based on the possible changes in the level of input and output of decision-making units. This is done to produce newly advanced based on the efficiency of these new units. In this research, after studying the P-DEA model thoroughly, we identified its drawbacks and proposed a new method for determining the practical boundary by developing an additive model using an example.

This paper proposes a DEA-based model for analyze the fuzzy risk in project selection. We used concept semi-variance for measure upper and downside risk and a DEA model for Classification desirable and undesirable risk. Firstly, the proposed model includes new desirable and undesirable risk-return indexes. Thus a novel DEA model is presented for evaluation and Classification desirable and undesirable risks and finally, is extend to fuzzy DEA model for project portfolio selection. An applied example is used to explain the proposed approach and usefulness and applicability of this approach have been illustrated using the 37 available projects.