A fuzzy Einstein-based decision support system for public transportation management at times of pandemic

Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving uncertainty management facilitating weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) methodology. Such a CoCoSo model improves previous proposals using nonlinear weighted functions for defining sequences. In addition, it proposes novel algorithm determining criteria weights based on logarithmic function, therefore allows decision-makers better perception relationship between as considers relationships adjacent criteria; high consistency expert comparisons; enables definition coefficients larger set without need cluster (group) criteria. Nonlinear implemented methodology enable processing complex information. characteristics contribute rational compromise strategies objective reasoning when solving real-world decision problems. The efficiency, effectiveness, robustness proposed illustrated case study create conceptual framework evaluate rank prioritization public transportation at time COVID-19 pandemic. results reveal its good performance systems strategy. • We consider selection method pandemic, COVID-19. 4 alternatives prioritized 13 main aspects. develop T-norm T-conorm (CoCoSo). apply additive function find out weights. Fuzzy averaging geometric is used.