Flexible and Generalized Uncertainty Optimization: Theory and Methods

Description

This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an  overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and that more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of such a model in detail. All in all, the book provides the readers with the necessary background to understand flexible and generalized uncertainty optimization and develop their own optimization model.  Front Matter....Pages i-x An Introduction to Generalized Uncertainty Optimization....Pages 1-35 Generalized Uncertainty Theory: A Language for Information Deficiency....Pages 37-69 The Construction of Flexible and Generalized Uncertainty Optimization Input Data....Pages 71-108 An Overview of Flexible and Generalized Uncertainty Optimization....Pages 109-134 Flexible Optimization....Pages 135-156 Generalized Uncertainty Optimization....Pages 157-175 Back Matter....Pages 177-190