Synopses & Reviews
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.
This book looks at the framework of the fuzzy random optimization including theoretical results, optimization models, intelligent algorithms, and case studies. It presents how to design the solution algorithms to these fuzzy random optimization problems.
Fuzzy Random Optimization: Theory, Models, and Algorithms introduces a new decision approach, namely, fuzzy random optimization, for dealing with the practical decision making problems under hybrid uncertainty bracing randomness and vagueness or fuzziness simultaneously. This book looks at the framework of the fuzzy random optimization including theoretical results, optimization models, intelligent algorithms, and case studies. Also this book presents how to design the solution algorithms to these fuzzy random optimization problems. This book focuses on a variety of fuzzy random optimization models such as continuous theorems, limit theorems, fuzzy random renewal theory, two-stage fuzzy random programming. The theoretical results introduced in Part I of this book are showed to be applied to the later optimization models (Part II) and practical applications (Part III). This book is written for both the researcher and the student.
About the Author
Dr. Shuming Wang
received his Ph.D in Engineering at WASEDA University, Japan, 2011. He was a Special Research Fellow of the Japan Society for the Promotion of Science (JSPS), Japan, and worked as a Researcher in Research Institute and Risk Management Division of China Galaxy Securities Co. LTD (HQ), Beijing, China. Currently, Dr. Wang is being with National University of Singapore (NUS) as a Research Fellow, he is also an Adjunct Researcher of WASEDA University, Japan.
Dr. Wang has published more than 20 international journal and conference papers in the fields of optimization under uncertainty, soft computing, and industrial engineering. He has also served as a referee for several international journals, including, IEEE Transactions on Systems, Man & Cybernetics: Systems, IEEE Transactions on Systems, Man & Cybernetics: Cybernetics, IEEE Transactions on Industrial Electronics, IEEE Transactions on Engineering Management, Annals of Operations Research, International Journal of Production Research, and Journal of Global Optimization.
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.
Table of Contents
Part I: Theory.- Fuzzy Random Variable.- Fuzzy Stochastic Renewal Processes.- Part II: Models.- System Reliability Optimization Models with Fuzzy Random Lifetimes.- Recourse-Based Fuzzy Random Facility Location Model with Fixed Capacity.- Two-Stage Fuzzy Stochastic Programming with Value-at-Risk: A Generic Model.- VaR-Based Fuzzy Random Facility Location Model with Variable Capacity.- Part III: Real-Life Applications.