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This book introduces methods for uncertain multi-attribute decision making including uncertain multi-attribute group decision making and their applications to supply chain management, investment decision making, personnel assessment, redesigning products, maintenance services, military system efficiency evaluation. Multi-attribute decision making, also known as multi-objective decision making with finite alternatives, is an important component of modern decision science. The theory and methods of multi-attribute decision making have been extensively applied in engineering, economics, management and military contexts, such as venture capital project evaluation, facility location, bidding, development ranking of industrial sectors and so on. Over the last few decades, great attention has been paid to research on multi-attribute decision making in uncertain settings, due to the increasing complexity and uncertainty of supposedly objective aspects and the fuzziness of human thought. This book can be used as a reference guide for researchers and practitioners working in e.g. the fields of operations research, information science, management science and engineering. It can also be used as a textbook for postgraduate and senior undergraduate students.
List of contents
Part 1 Real-Valued MADM Methods and Their Applications.- Real-Valued MADM with Weight Information Unknown.- MADM with Preferences on Attribute Weights.- MADM with Partial Weight Information.- Part 2 Interval MADM Methods and Their Applications.- Interval MADM with Real-Valued Weight Information.- Interval MADM with Unknown Weight Information.- Interval MADM with Partial Weight Information.- Part 3 Linguistic MADM Methods and Their Applications.- Linguistic MADM with Unknown Weight Information.- Linguistic MADM Method with Real-Valued or Unknown Weight Information.- MADM Method Based on Pure Linguistic Information.- Part 4 Uncertain Linguistic MADM Methods and Their Applications.- Uncertain Linguistic MADM with Unknown Weight Information.- Uncertain Linguistic MADM Method with Real-Valued Weight Information.- Uncertain Linguistic MADM Method with Interval Weight Information.
About the author
Zeshui Xu received the PhD degree in management science and engineering from Southeast University, Nanjing, China, in 2003. From April 2003 to May 2005, he was a Postdoctoral Researcher with the School of Economics and Management, Southeast University. From October 2005 to December 2007, he was a Postdoctoral Researcher with the School of Economics and Management, Tsinghua University, Beijing, China. From November 2008 to February 2009, February 2010 to May 2010, February 2011 to May 2011, and on August 2009, he was a Visiting Research Scholar at the Chinese University of Hong Kong, Shatin, N.T., Hong Kong. He is currently an IEEE Senior Member, Reviewer of Mathematical Reviews of American Mathematical Society, and an Adjunct Professor with the Antai School of Economic and Management, Shanghai Jiaotong University, Shanghai, China; an Adjunct Professor with the School of Economics and Management, Southeast University Nanjing, Jiangsu, China. He is also currently a Chair Professor with the Sciences Institute, PLA University of Science and Technology, Nanjing, China. He serves on the Editorial Boards of Information: An International Journal, International Journal of Applied Management Science, International Journal of Data Analysis Techniques and Strategies, System Engineering Theory and Practice, Journal of Systems Engineering, and Fuzzy Systems and Mathematics. He has authored three books, and has contributed more than 300 journal articles to professional journals, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics, Information Sciences, European Journal of Operational Research, Omega, Decision Support Systems, Journal of Optimization Theory and Applications, Fuzzy Sets and Systems, Group Decision and Negotiation, International Journal of Approximate reasoning, Information Fusion, International Journal of Intelligent Systems, Computers & Industrial Engineering, Expert Systems With Applications, Soft Computing, Applied Soft Computing, Knowledge-Based Systems, International Journal of General Systems, Fuzzy Optimization and Decision Making, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, and International Journal of Information Technology and Decision Making, etc. His current research interests include information fusion, group decision making, computing with words, intuitionistic fuzzy sets, and aggregation operators.
