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In 2014, winner of "Outstanding Book Award" by The Japan Society
for Fuzzy Theory and Intelligent Informatics. 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.
In 2014, winner of "Outstanding Book Award" by The Japan Society
for Fuzzy Theory and Intelligent Informatics. 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.
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