Decision's problem with the help of imprecise data envelopment analysis (IDEA)

The standard data envelopment analysis (DEA) method requires that the values for all inputs and outputs be known exactly. When some inputs and outputs are unknown decision variables such as bounded data, original data, and ratio bounded data, the DEA model becomes a non-linear programming problem and is called imprecise DEA (IDEA). In this study we aim to develop further the IDEA by using a kind of punishment-penalty in the DMUs (Decision Making Units) which try on their own to weight the surges and the flows in order to elect in the major degree their efficiency. This is attempted in order to avoid the fact that the record of an efficient unit is arguable.