Lodwick W.A., de Salles Neto L.L. Flexible and Generalized Uncertainty Optimization: Theory and Approaches

2nd Edition. – Springer, 2021. — 201 p. — (Studies in Computational Intelligence 696). — ISBN: 978-3-030-61179-8.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 are 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 the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.An Introduction to Generalized Uncertainty Optimization
Generalized Uncertainty Theory: A Language for Information Deficiency
The Construction of Flexible and Generalized Uncertainty Optimization Input Data
An Overview of Flexible and Generalized Uncertainty Optimization
Flexible Optimization
Generalized Uncertainty Optimization