Several Intuitionistic Fuzzy Multi-Attribute Decision Making Methods and Their Applications

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· Springer Nature
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This book introduces readers to the latest advances in and approaches to intuitionistic fuzzy decision-making methods. To do so, it explores a range of applications to practical decision-making problems, together with representative case studies. Examining a host of decision-making methods, most of which are based on intuitionistic fuzzy aggregation operators, its goal is to offer readers a new way to study decision-making methods in the intuitionistic fuzzy environment. Chiefly intended for practitioners and researchers working in the areas of risk management, decision-making under uncertainty, and operational research, the book can also be used as supplementary material for graduate and senior undergraduate courses in these areas.

Tietoja kirjoittajasta

Zhinan Hao received the Ph.D. degree in computer science and technology from Army Engineering University of PLA, Nanjing, China, in 2018. He has contributed 4 journal articles to professional journals, and his current research interests include decision making and fuzzy sets.
Zeshui Xu received the Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2003. He is a Distinguished Young Scholar of the National Natural Science Foundation of China and the Chang Jiang Scholars of the Ministry of Education of China. He is currently a Professor with Business School, Sichuan University, Chengdu, China. He has contributed more than 450 journal articles to professional journals, and his current research interests include information fusion, decision making, and fuzzy sets.

Hua Zhao received the Ph.D. degree in military operation research from PLA University of Science and Technology, Nanjing, China, in 2011. She is currently an Associated Professor with Department of General Education, Army Engineering University of PLA, Nanjing, China. She has contributed more than 30 journal articles to professional journals, and her current research interests include clustering analysis, decision making and fuzzy sets.

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