Imprecise Data Envelopment Analysis (IDEA): a review and a new approach

All today's Decision Making Units' (DMUs) strategic goal is to become efficient. Efficiency is defined as the ability of a DMU to produce maximum output using the minimum possible input. DEA (Data Envelopment Analysis) is a very useful tool for evaluating efficiency. This method requires that the values for all inputs and outputs are known exactly. However, the input and output values are initially unknown and are limited to intervals. This model is called as the IDEA (Imprecise Data Envelopment Analysis) model. In this paper we develop an alternative approach for dealing with imprecise DEA. Our approach is to transform a non-linear DEA model to a linear programming equivalent on the basis of the original data set, by applying transformation only to the variables. Then we proceed still further in formulating another post-DBA model for limiting the large intervals of DMUs in output level as well as in input level (saving resources) without affecting DMUs' efficiency.