determining minor and major consolidations in network inverse data envelopment analysis

It is necessary to make use of scientific methods when merging the decision-making units (dmus) in any organization. tools such as data envelopment analysis (dea) and network dea (ndea) are quite useful for unit mergers in two-stage network processes. in this paper, a two-stage network inverse dea (invdea) process is proposed for the merger of university and bank branches based on linear programming models. it is generally crucial to prioritize the inputs and outputs and find the intermediate vectors in multi-stage networks. therefore, a two-stage network inverse dea model is used for the purposes of this study. finally, some applications of the proposed model are provided in dmu mergers based on vector prioritization using shannon’s entropy, namely the mergers of 5 universities, 24 insurance companies, and 20 commercial banks.