Bivariate models of optimism and pessimism in multi-criteria decision-making based on intuitionistic fuzzy sets

This paper presents a useful method of relating optimism and pessimism to multiple criteria decision analysis in the context of intuitionistic fuzzy sets based on the unipolar bivariate model. We use eight point operators to estimate the adaptational outcome expectations of optimism and/or pessimism and then determine the net predisposition, the aggregated effect of positive and negative evaluations. A series of new net predispositions for bivariate evaluations are proposed for neutrality; for complete, moderate, and rational optimism; for complete, moderate, and rational pessimism; and for complete and moderate optimism-pessimism. The suitability function, which measures the overall evaluation of each alternative, is then presented. Because positive or negative leniency may exist, such that most of the criteria may be assigned unduly high or low ratings, respectively, we introduce deviation variables to mitigate the effects of such ratings on the apparent importance of various criteria. Based on the two objectives of maximal weighted suitability and minimal deviation values, an integrated programming model is used to compute the optimal weights for the criteria and the corresponding degrees of suitability of the alternative rankings. We establish flexible algorithms that incorporate both objective and subjective information to compute the optimal optimistic and pessimistic decisions. The proposed methods are illustrated and discussed using a numerical example, a multi-criteria supplier selection problem. Finally, an empirical study of job choices is conducted to establish the feasibility and applicability of the current method.