Possibilistic Network DEA Approach for Performance Evaluation of Two-Stage Decision Making Units Under Uncertainty

Uncertainty is a significant context to investigate when assessing entities. In the presence of imprecise and vague data, this study presents a novel method for evaluating the performance of decision-making units (DMUs) utilizing a network structure consisting of two stages. To present the fuzzy network data envelopment analysis (FNDEA) model, two-stage data envelopment analysis (TSDEA), chance-constrained programming (CCP), and possibilistic programming are employed. Furthermore, the possibilistic network data envelopment analysis (PNDEA) method  can be utilized under various returns to scale (RTS) assumptions. To measure the performance of investment firms (IFs) using a two-stage structure that includes portfolio and operational management procedures, the developed fuzzy network DEA model is implemented. In addition, IFs like mutual funds (MFs) and investment organizations are extremely significant organizations to make investments in capital markets. Consequently, assessing the performance of these firms to identify efficient IFs and propose appropriate solutions for inefficient IFs is important. Finally, a real-world case study from the Tehran Stock Exchange is used, and the findings indicate that the developed model is effective.