Book 368

This book offers a comprehensive overview of cutting-edge approaches for decision-making in hierarchical organizations. It presents soft-computing-based techniques, including fuzzy sets, neural networks, genetic algorithms and particle swarm optimization, and shows how these approaches can be effectively used to deal with problems typical of this kind of organization. After introducing the main classical approaches applied to multiple-level programming, the book describes a set of soft-computing techniques, demonstrating their advantages in providing more efficient solutions to hierarchical decision-making problems compared to the classical methods. Based on the book Fuzzy and Multi-Level Decision Making (Springer, 2001) by Lee E.S and Shih, H., this second edition has been expanded to include the most recent findings and methods and a broader spectrum of soft computing approaches. All the algorithms are presented in detail, together with a wealth of practical examples and solutions to real-world problems, providing students, researchers and professionals with a timely, practice-oriented reference guide to the area of interactive fuzzy decision making, multi-level programming and hierarchical optimization.


v. 6

This volume gives readers a complete picture of fuzzy approximate reasoning and uncertainty reasoning based on evidence theory. The topics are systematically treated from fundamentals to advanced issues. Numerous examples illustrate the concepts. The work decribes and analyzes various approaches regarding fuzzy approximate reasoning including fuzzy logics, uncertainty representation, and fuzzy inference patterns. Approximate reasoning based on evidence theory is mainly resulted from the authors' own research work. The book can be used as a textbook for courses in uncertainty reasoning as well as a quick reference on techniques for managing uncertainty for any one working in the areas of expert systems.