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An Intuitionistic Fuzzy Entropy Measure and Its Application in Multi-Attribute Decision Making With Incomplete Weights Information
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Author(s): Reeta Bhardwaj (Department of Mathematics, Amity University Haryana, Gurugram, India), Amit Sharma (Department of Mathematics, Amity University Haryana, Gurugram, India), Naveen Mani (Chandigarh University, Chandigarh, India)and Kamal Kumar (Department of Mathematics, Amity University Haryana, Gurugram, India)
Copyright: 2022
Pages: 15
Source title:
Handbook of Research on Advances and Applications of Fuzzy Sets and Logic
Source Author(s)/Editor(s): Said Broumi (Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, Casablanca, Morocco & Regional Center for the Professions of Education and Training (CRMEF), Casablanca-Settat, Morocco)
DOI: 10.4018/978-1-7998-7979-4.ch015
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Abstract
Fuzzy sets, as well as their extension intuitionistic fuzzy sets (IFS), are more effective and appealing tools for expressing quantitative complexity throughout the decision-making process, and they have gotten greater attention from researchers in recent years for new study directions. Keeping the advantages of IFS, this chapter proposes a novel information measure to measure the fuzziness of IFS known as entropy measure (EM). The various notable features of the proposed EM are also presented. EM is a very useful tool to determine the attribute's weight during the multi-attribute decision-making (MADM) process. Therefore, in this chapter, the authors propose a new MADM approach by using the proposed EM where the attribute's weights are absolutely unknown. Finally, the proposed MADM methodology is applied to solve the real-life MADM examples. Comparative studies are also given to show the advantages of the proposed MADM methodology. The proposed MADM methodology can overcome the weaknesses of the existing MADM approaches.
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