Taxonomy-based multiple attribute group decision making method with probabilistic uncertain linguistic information and its application in supplier selection

TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection

At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately. Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics center site selection (MLCSS) is given to validate the proposed method.

Taxonomy method for green competitiveness evaluation of equipment manufacturing enterprises under picture 2-tuple linguistic environment

The fourth industrial revolution, represented by intelligence, networking and greening, is breeding and breaking through, and China is increasingly Focus on green development, public awareness of environmental protection, and small changes are able to fundamentally change our industrial organization. The equipment manufacturing industry is an industry to which our country attaches great importance, and equipment manufacturing enterprises, as microscopic subjects, want to contribute to the “13th Five-Year Plan”. Equipment manufacturing industry is a highly valued industry in China, equipment manufacturing enterprises as a microscopic subject, in order to the “Thirteenth Five-Year Plan” equipment manufacturing industry rose to inject a strong impetus, must make essential changes, embrace the green economy, green development, and enhance the green competitiveness of enterprises. The green competitiveness evaluation of equipment manufacturing enterprises is the classical multiple criterion group decision making (MCGDM). In this paper, depending on the Taxonomy method, Taxonomy method will be extended to picture 2-tuple linguistic sets (P2TLSs) to address some MCGDM issues. Some essential concepts of picture 2-tuple linguistic sets (P2TLSs) are briefly reviewed. In addition, integrating the Taxonomy method with P2TLSs, the Taxonomy method with P2TLNs is constructed and all calculating procedures are simply depicted. Eventually, an empirical application for green competitiveness evaluation of equipment manufacturing enterprises has been offered to demonstrate this novel method and some comparative analysis are also made to confirm the merits of developed method.

IVNN-Taxonomy method for teaching effect evaluation of “micro-ideological and political” model in medical colleges and universities

How to make good use of new network technology and design the classroom teaching of a course needs to be based on the teaching object, teaching content, and the teacher’s mastery of the technology and teaching platform. In teaching design, scholars also put forward different teaching links based on their own teaching experience. The cooperative learning links should be designed in college teaching. To build a positive and interdependent organizational structure and an equal and democratic learning atmosphere will help students to stimulate their learning motivation and sense of responsibility. The fuzzy evaluation of the teaching effect of the “micro-ideological and political” model in medical colleges and universities is viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonomy method is designed for solving the MAGDM under interval-valued neutrosophic sets (IVNSs). First, the score function of IVNSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) method is built to deal with MAGDM problem. Finally, a numerical example for teaching effect evaluation of the “micro-ideological and political” model in medical colleges and universities is given to illustrate the built method.

An integrated decision support taxonmy method using probabilistic double hierarchy linguistic MAGDM for physical health literacy evaluation of college students

Health literacy is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health literacy evaluation of College students is frequently viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonmy method is designed for solving the MAGDM under probabilistic double hierarchy linguistic term sets (PDHLTSs). First, the expected function of PDHLTSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest probabilistic double hierarchy linguistic development attribute values from the probabilistic double hierarchy linguistic positive ideal solution (PDHLPIS). Finally, a numerical example for physical health literacy evaluation of College students is given to illustrate the built method.

Extended EDAS method for multiple attribute decision making in mixture z-number environment based on CRITIC method

In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems.