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Fuzzy Stochastic Optimization: Theory, Models and Applications

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Fuzzy Stochastic Optimization: Theory, Models and Applications Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

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.

Synopsis:

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. 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

Ch 1. Introduction.- Ch 2. Preliminaries.- Ch 3. Fuzzy Random Variable.- Ch 4. Fuzzy Random Renewal Theory.- Ch 5. Single-Stage Fuzzy Random Optimization Models.- Ch 6. Two-Stage Fuzzy Random Optimization Models.- Ch 7. Fuzzy Random Optimal Replacement Policy Problems.- Ch 8. Fuzzy Random Reliability Optimization Problems.- Ch 9. Two-Stage Fuzzy Random Facility Location Problems

Product Details

ISBN:
9781441995599
Author:
Wang, Shuming
Publisher:
Springer
Author:
Watada, Junzo
Location:
New York, NY
Subject:
Artificial Intelligence
Subject:
Fuzzy Optimization
Subject:
Fuzzy Random Variable
Subject:
Industrial Informatics
Subject:
Intelligent Computation
Subject:
Management Engineering
Subject:
metaheuristics
Subject:
Operational Research
Subject:
Renewal Process
Subject:
Renewal Reward Process
Subject:
Computational intelligence
Subject:
OPTIMIZATION
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Engineering-General Engineering
Subject:
Engineering
Subject:
B
Subject:
Mathematical optimization
Copyright:
Edition Description:
2012
Publication Date:
20110501
Binding:
HARDCOVER
Language:
English
Pages:
266
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Engineering » Engineering » General Engineering
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Fuzzy Stochastic Optimization: Theory, Models and Applications New Hardcover
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Product details 266 pages Springer - English 9781441995599 Reviews:
"Synopsis" by , 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.
"Synopsis" by , 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.
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