A Computational Framework for Accelerating DEA

Data envelopment analysis (DEA) is a mathematical programming technique for identifying relative efficiency scores of decision making units (DMUs). The ability of identifying the equations of efficient facets (efficient frontier) prior to the DEA calculation is of extreme importance to an effective and efficient DEA computation.The equations of these facets are useful in efficiency evaluations, sensitivity and stability analysis, identification of returns to scale (RTS) characteristic of each DMU, and etc. In this paper we introduce a new computational framework for identifying efficient frontier of production possibility set (PPS) in DEA models.