Abstract
The aim of this chapter is to provide a very concise overview of Data Envelopment Analysis (DEA) and its subsequent improvements. DEA proposed by Charnes et al. (Eur J Oper Res, 2:429–444, 1978) and based on the seminal article by Farrell (J Roy Stat Soc 120:253–290, 1957) aims to develop a comparative measure for production efficiency. We present first a brief history of the development of DEA. Next, we make a comparison of DEA and stochastic frontier analysis (SFA). DEA is a nonparametric and deterministic approach, whereas SFA is a parametric and stochastic approach. We also focus on the history of the development of the efficiency-improvement projection model in DEA. The existence of many possible efficiency-improvement solutions has in recent years prompted a rich literature on the methodological integration of multiple objective quadratic programming (MOQP) and DEA models. The first contribution was made by Golany (J Oper Res Soc 39:725–734, 1988), and we introduce here a concise overview of the history of the development of efficiency-improvement projection models in DEA. Based on these backgrounds, we present advantages and features of our DFM (distance friction minimization) model.
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