3 research outputs found
PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection
With the rapid development of 3D printing technology, 3D printers are manufactured based on the principle of 3D printing technology are more and more widely used in the manufacturing industry. Choosing high quality 3D printers for industrial production is of great significance to the economic growth of enterprises. In fact, it is difficult to select the most optimal 3D printers under a single and simple standard. Therefore, this paper establishes the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for the multiple attribute group decision making (MAGDM). Then the CRITIC model is introduced to derive objective weight and the cumulative prospect theory is leaded into obtain the cumulative weight of PDHLTS. In addition, what’s more, the PDHL-EDAS method is built and applied to the choice of high-quality 3D printer. Finally, compared with the available MAGDM methods under PDHLTS, the built method is proved to be scientific and effective.
First published online 15 December 202
EDAS method for multiple criteria group decision making with picture fuzzy information and its application to green suppliers selections
In this paper, we construct picture fuzzy EDAS model based on traditional EDAS (Evaluation based on Distance from Average Solution) model. Firstly, we briefly review the definition of picture fuzzy sets (PFSs) and introduce the score function, accuracy function and operational laws of picture fuzzy numbers (PFNs). Then, we combine traditional EDAS model for MCGDM with PFNs. In our model, it’s more accuracy and effective for considering the conflicting attributes. Finally, a numerical example for green supplier selection has been given to illustrate this new model and some comparisons between EDAS model with PFNs and PFWA, PFWG aggregation operators are also conducted to further illustrate advantages of the new method.
First published online 23 August 201
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