Abstract
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.
Highlights
We can know that EDAS method is an efficient method to deal with multiple attribute group decision making (MAGDM) issue, but there is no research on EDAS method under probabilistic double hierarchy linguistic term set (PDHLTS) in the Technological and Economic Development of Economy, 2022, 28(1): 179–200 existing literatures, in order to effectively solve the problem of 3D printer selection, this paper will construct an PDHL-EDAS method to deal with this problem
The main work of this paper could be showed: (1) the PDHL-EDAS method for MAGDM is established; (2) the CRITIC (Diakoulaki et al, 1995) method is used to calculate the objective weight of attributes under PDHLTSs; (3) the steps of solving MAGDM problem with PDHL-EDAS model are given; (4) the PDHL-EDAS model is used to select the best 3D printer; (5) the model constructed in this paper is compared with the existing models
The selection of optimal 3D printers is a great significance in the production and sales process of enterprises
Summary
For example, the frequency or probabilistic of “just right poor” is 0.3, and “only a little good” is 0.7 is can not represent by those sets To solve this issue the probabilistic double hierarchy linguistic term set (PDHLTS) proposed by Gou, Xu, Liao, and Herrera (2021). He et al (2019) built the EDAS algorithm for MAGDM with PULTSs. From the above description, we can know that EDAS method is an efficient method to deal with MAGDM issue, but there is no research on EDAS method under PDHLTSs in the Technological and Economic Development of Economy, 2022, 28(1): 179–200 existing literatures, in order to effectively solve the problem of 3D printer selection, this paper will construct an PDHL-EDAS method to deal with this problem. The main work of this paper could be showed: (1) the PDHL-EDAS method for MAGDM is established; (2) the CRITIC (Diakoulaki et al, 1995) method is used to calculate the objective weight of attributes under PDHLTSs; (3) the steps of solving MAGDM problem with PDHL-EDAS model are given; (4) the PDHL-EDAS model is used to select the best 3D printer; (5) the model constructed in this paper is compared with the existing models
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