A NEW INVERSE DEA MODEL FOR UNITS RESTRUCTURING: A CASE STUDY OF COMMERCIAL BANKS MEMBERS OF THE PERSIAN GULF CORPORATION COUNCIL (GCC)

A mathematical programming based non-parametric technique for the performance assessment of decision-makingunits (DMUs) with multiple inputs and outputs is considered in a general framework called data envelopment analysis (DEA). DEA models have been utilized for estimating the eciency scores of the DMUs with certain input-output levels. However, in the last two decades, various studies have been concentrated on the inverse DEA as an analytical framework of DEA to nd the required inputs and outputs levels for achieving a predetermined eciency target. The main aim in the inverse DEA is to estimate the inputs and/or outputs for a special DMU to attain a given eciency target while evaluating the performance of a specic DMU is the main objective in the DEA analysis. Inverse DEA has been studied from both theoretical and practical aspects including sensitivity analysis, resource allocation, preserving or improving eciency scores, and merging DMUs to achieve the predetermined eciency target. Also, inverse DEA has been employed for modeling generalized restructuring DMUs. In a generalized restructuring a set of pre-restructuring DMUs through consolidation/split to create synergy/reverse synergy, proceed with a restructuring to produce a new set of post-restructuring DMUs to achieve predened eciency targets.
This paper deals with the problem of units' restructuring using inverse data envelopment analysis (DEA). A generalized restructuring refers that a set of decision-making units based on synergies through mergers/acquisitions or reverse synergies through split, proceed with a restructuring to produce a new set of post-restructuring entities to improve eciency. The problem of units' restructuring is investigated in this paper, and to achieve a pre-specied eciency level for each post-restructuring decision-making units, models for estimating inputs (outputs) have been proposed. The most important advantages of the proposed models, compared to other the provided models, as follows: i) Due to the use of multiobjective programming (MOP) tools, allows the decision maker to pursue multiple goals in the problem of restructuring units. ii) The proposed method has less computational complexity because the number of variables is greatly reduced. In addition, a numerical example with real data is employed to evaluate the performance of the proposed models.