Fuzzy Stochastic Optimization

Theory, Models and Applications

By Shuming Wang , Junzo Watada

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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.

Full Description

  • ISBN13: 978-1-4419-9559-9
  • 266 Pages
  • Publication Date: March 20, 2012
  • Available eBook Formats: PDF

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Full Description
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.
Table of Contents

Table of Contents

  1. Part I: Theory.
  2. Fuzzy Random Variable.
  3. Fuzzy Stochastic Renewal Processes.
  4. Part II: Models.
  5. System Reliability Optimization Models with Fuzzy Random Lifetimes.
  6. Recourse
  7. Based Fuzzy Random Facility Location Model with Fixed Capacity.
  8. Two
  9. Stage Fuzzy Stochastic Programming with Value
  10. at
  11. Risk: A Generic Model.
  12. VaR
  13. Based Fuzzy Random Facility Location Model with Variable Capacity.
  14. Part III: Real
  15. Life Applications.

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