Hesitant Fuzzy Decision Making Methodologies and Applications

This book offers a comprehensive and systematic introduction to the latest research on hesitant fuzzy decision-making theory. It includes six parts: the hesitant fuzzy set and its extensions, novel hesitant fuzzy measures, hesitant fuzzy hybrid weighted aggregation operators, hesitant fuzzy multiple-criteria decision-making with incomplete weights, hesitant fuzzy multiple criteria decision-making with complete weights information, and the hesitant fuzzy preference relation based decision-making theory. These methodologies are implemented in various fields such as decision-making, medical diagnosis, cluster analysis, service quality management, e-learning management and environmental management. A valuable resource for engineers, technicians, and researchers in the fields of fuzzy mathematics, operations research, information science, management science and engineering, it can also be used as a textbook for postgraduate and senior undergraduate students.

Offers a self-contained introduction to the field of hesitant fuzzy decision-making Presents state-of-the-art in hesitant fuzzy decision-making methodologies and applications Written by leading experts in the field of operation research and decision theory Includes supplementary material: sn.pub/extras

Autorentext

Huchang Liao is an associate research fellow at Sichuan University, in Chengdu, China. He was an academic visitor at the School of Computer Science, University of Manchester, UK. He was an Endeavour Research Fellow at the University of Technology Sydney in 2015. He has published one book and more than 40 peer-reviewed papers, many in high-quality international journals including IEEE Transactions on Fuzzy Systems, Information Sciences, Knowledge-Based Systems, Applied Soft Computing, Expert Systems with Applications, etc. Ten of his papers have been selected as ESI highly cited papers. His current research interests include group decision-making, computing with words, information fusion, and multiple criteria decision-making under uncertainty. Dr. Liao is the Associate Editor of the International Journal of Fuzzy Systems, and serves as a reviewer for more than 40 international journals. His biographical profile has been selected for inclusion in the Marquis Who's Who in theWorld in 2015 and 2016. Zeshui Xu 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 the Business School, Sichuan University, Chendu, China. He has been selected as a 2014 and 2015 Thomson Reuters Highly Cited Researcher (in the fields of Computer Science and Engineering, respectively) and also included in The World's Most Influential Scientific Minds 2014. His h-index is 87, and he has authored the following books: Uncertain Multiple Attribute Decision-making: Methods and Applications (Tsinghua Univ. Press, 2004); Intuitionistic Fuzzy Information Aggregation: Theory and Applications (Science Press and Springer, 2012), Linguistic Decision-making: Theory and Methods (Science Press and Springer, 2012); Intuitionistic Fuzzy Preference Modeling and Interactive Decision-making (Springer, 2013); Intuitionistic Fuzzy Aggregation and Clustering (Springer, 2013); and Hesitant Fuzzy Sets Theory (Springer, 2014). He has contributed more than 450 articles to professional journals. His current research interests include information fusion, group decision-making, computing with words, and aggregation operators. Prof. Xu is the Associate Editor of Fuzzy Optimization and Decision-making, the Journal of Intelligence Systems, the Section Editor of the Asian Journal of Social and Economic Sciences, and the Chief Editor of the Scholars Journal of Economics, Business and Management.

Inhalt
Preface.- 1 Hesitant Fuzzy Set and Its Extensions.- 2 Novel Correlation and Entropy Measures for Hesitant Fuzzy Set.- 3 Multiple Criteria Decision Making with Hesitant Fuzzy Hybrid Weighted Aggregation Operators.- 4 Hesitant Fuzzy Multiple Criteria Decision Making with Complete Weight Information.- 5 Hesitant Fuzzy Multiple Criteria Decision Making with Incomplete Weights.- 6 Decision Making with Hesitant Fuzzy Preference Relation.