Merging decision-making units with interval data

This paper deals with the problem of merging units with interval data. There are two important problems in the merging units. Estimation of the inherited inputs/outputs of the merged unit from merging units is the first problem while the identification of the least and most achievable efficiency targets from the merged unit is the second one. In the imprecise or ambiguous data framework, the inverse DEA concept and linear programming models could be employed to solve the first and second problem, respectively. To identify the required inputs/outputs from merging units, the merged entity is enabled by the proposed method. This provides a predefined efficiency goal. The best and worst attainable efficiency could be determined through the presented models. The developed merging theory is evaluated through a banking sector application