Novel Hesitant Fuzzy Linguistic Multi-Attribute Group Decision Making Method Based on Improved Supplementary Regulation and Operational Laws

The aim of this paper is to develop some novel operational laws for a hesitant fuzzy linguistic term set (HFLTS)-based on the improved supplementary regulation and Archimedean t-norms and s-norms. The improved supplementary regulation for HFLTSs can reserve the fidelity of original information commendably, and it brings a new conception to research on information measures of HFLTS. As a hot and key research topic for information fusion, some Archimedean t-norms and s-norms based hesitant fuzzy linguistic aggregation operators are proposed to aggregate HFLTSs. Some essential properties together with their special cases of such aggregation operators are discussed in detail. The entropy and cross-entropy of HFLTSs are proposed and applied to derive the attribute weights. An approach to multiple attributes group decision making with hesitant fuzzy linguistic information is developed. Finally, a numerical example related to the assessment of health-care waste disposal methods is provided to show the utility and effectiveness of our methods, which are then compared to the existing methods.