Searching the Efficient Frontier in Data Envelopment Analysis
In this paper, we deal with the problem of searching the efficient frontier in Data Envelopment Analysis (DEA). Our aim is to show that the free aim approach developed to make a search on the efficient frontier in multiple objective programming can also be used in DEA. This kind of analysis is needed when among others a) a radial projection is not acceptable, b) there are restrictions on some input and output values, or c) a decision maker (DM) would like to make a decision making unit (DMU) with the most preferred input and output values. The search can be applied to CCR/BCC-models, input-oriented/output-oriented models, and to the models with extra constraints. To make a free search on the efficient frontier, we recommend the use of Pareto Race (Korhonen and Wallenius ) for this purpose. In Pareto Race, the DM may simply control the search with some function keys. The information is displayed to the DM as bar graphs and in numeric form. The search can be terminated at any time, the DM wishes. A numerical example is used to illustrate the approach.
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