2-Tuple unbalanced linguistic multiple-criteria group decision-making using prospect theory data envelopment analysis

The linguistic terms used to describe the qualitative data in problems are symmetrical about the central linguistic word. With the increasing complexity and a large number of DMs with different domain expertise involved in decision-making, the symmetric linguistic term set seems constricted. This paper examines the multiple-criteria group decision-making problems in which the decision-makers use the unbalanced 2-tuple linguistic term set to describe the alternative-criteria assessments matrices. We adopt a data envelopment analysis (DEA) approach and develop a linear programming model to evaluate alternatives criteria weights for each decision-maker. The non-rational factor of risk in criteria gets modeled using the value function from the prospect theory. The values of prospect gain and prospect loss, respectively, on cost and benefit parameters are computed and applied to formulate a DEA model to assess the weights of each criterion on each alternative. Eventually, the cross-efficiency scores and their entropy values provide a ranking of the alternatives. A numerical example is presented to illustrate the methodology. A comparative analysis of the proposed method with fuzzy ordinal priority approach and fuzzy TOPSIS is performed for the illustrative example. Finally, an application example of ranking the faculties on their end-semester feedback from their class students in an academic institute is presented.