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Examines statistical methods with fuzzy data based on the fuzzy random variable
Covers how to characterize information which is both probabilistically uncertain and fuzzily imprecise
Discusses building optimization models working with uncertain data
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.
Content Level »Research
Keywords »Fuzzy Optimization - Fuzzy Random Variable - Industrial Informatics - Intelligent Computation - Management Engineering - Meta-heuristics - Operational Research - Renewal Process - Renewal Reward Process
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.