Fuzzy Stochastic Optimization: Theory, Models and Applications - Tapa dura

Acerca del autor

Dr. Shuming Wang received his Ph.D in Engineering at Waseda University, Japan. He was a Special Research Fellow of Japan Society for the Promotion of Science (JSPS) from 2009/04 until 2011/03. Currently, Dr. Wang is being with China Galaxy Securities Company as a Research Fellow, Beijing, China, he is also a Visiting Research Fellow of Waseda University, Japan. Dr. Wang has published more than 20 international journal and conference papers in the fields of soft computing, operational research, and management engineering. He has also served as a referee for several international journals, including, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans (IEEE TSMC-A), IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (IEEE TSMC-B), Journal of Computational & Applied Mathematics (JCAM), Mathematical & Computer Modelling (MCM), and International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems (IJUFKS). Dr. Junzo Watada is currently a full professor of Management Engineering, Knowledge Engineering and Soft Computing at Graduate School of Information, Production & Systems, Waseda University. He is the Principal Editor, a Co-Editor and an Associate Editor of various international journals, including International Journal of Biomedical Soft Computing and Human Sciences, ICIC Express Letters, International Journal of Systems and Control Engineering, and Fuzzy Optimization & Decision Making.

De la contraportada

Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies.

The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins by outlining the history and development of the fuzzy random variable before detailing numerous optimization models and applications that include the design of system controls for a dam.