scholarly journals Multi-Attribute Group Decision Making Based on Intuitionistic Uncertain Linguistic Hamy Mean Operators With Linguistic Scale Functions and Its Application to Health-Care Waste Treatment Technology Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 20-46 ◽  
Author(s):  
Zhengmin Liu ◽  
Hongxue Xu ◽  
Xiaolan Zhao ◽  
Peide Liu ◽  
Junqing Li
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Group Decision Making ◽  
Waste Treatment ◽  
Group Decision ◽  
Technology Selection ◽  
Treatment Technology ◽  
Scale Functions ◽  
Health Care Waste

scholarly journals Multiple attribute group decision-making based on interval-valued q-rung orthopair uncertain linguistic power Muirhead mean operators and linguistic scale functions

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258772
Author(s):  
Yuan Xu ◽  
Shifeng Liu ◽  
Jun Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Comparison Method ◽  
Model Framework ◽  
Scale Functions ◽  
Multiple Attribute ◽  
Definition Of ◽  
Magdm Problems ◽  
Interval Valued

Fuzzy set theory and its extended form have been widely used in multiple-attribute group decision-making (MAGDM) problems, among which the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) got a lot of attention for its ability of capturing information denoted by interval values. Based on the previous studies, to find a better solution for fusing qualitative quantization information with fuzzy numbers, we propose a novel definition of interval-valued q-rung orthopair uncertain linguistic sets (IVq-ROULSs) based on the linguistic scale functions, as well as its corresponding properties, such as operational rules and the comparison method. Furthermore, we utilize the power Muirhead mean operators to construct the information fusion method, and provide a variety of aggregation operators based on the proposed information description environment. A model framework is constructed for solving the MAGDM problem utilizing the proposed method. Finally, we illustrate the performance of the new method and investigate its advantages and superiorities through comparative analysis.

Cloud decision support framework for treatment technology selection of health-care waste

2021 ◽  
pp. 1-26
Author(s):  
Rui-Lu Huang ◽  
Min-hui Deng ◽  
Yong-yi Li ◽  
Jian-qiang Wang ◽  
Jun-Bo Li
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Assessment Process ◽  
Technology Selection ◽  
Human Beings ◽  
Treatment Technology ◽  
Decision Support Framework ◽  
Health Care Waste ◽  
Evaluation Information ◽  
Selection Of

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods.

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