Modified EDAS Method Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-valued Intuitionistic Fuzzy Information

The Interval-valued intuitionistic fuzzy sets (IVIFSs) based on the intuitionistic fuzzy sets combines the classical decision method is in its research and application is attracting attention. After comparative analysis, there are multiple classical methods with IVIFSs information have been applied into many practical issues. In this paper, we extended the classical EDAS method based on cumulative prospect theory (CPT) considering the decision makers (DMs) psychological factor under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original EDAS method based on the CPT under IVIFSs (IVIF-CPT-MABAC) method is built for MAGDM issues. Meanwhile, information entropy method is used to evaluate the attribute weight. Finally, a numerical example for project selection of green technology venture capital has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new methods effectiveness and stability.Comment: 48 page

CODAS method with Probabilistic hesitant fuzzy information and its application to environmentally & economically balanced supplier selection

With the rise of the concept of environmental protection and the attention of all sectors of society to the ecological environment, the selection of green suppliers is a hot topic. In this paper, we develop the combinative distance-based assessment (CODAS) method in the probabilistic hesitant fuzzy sets (PHFSs) to cope with the multiple attributes group decision making (MAGDM). A standardized approach that integrates multiple methods is applied to normalize the original data. Moreover, the statistics variance (SV) method is applied under PHFSs to calculate the objective weighting vector of evaluation criteria. In the end, a case for supplier selection and the comparative analysis are used to confirm the feasibility and utility of this new approach. First published online 30 August 202