Flexible and Generalized Uncertainty Optimization
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 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.
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Luiz L. Salles-Neto received the M.Sc. degree in mathematics and the Ph.D. degree in computational and applied mathematics from the University of Campinas, Brazil, in 2000 and 2005, respectively. He was a Research Scholar at the Universidad de Sevilla, Spain, in 2009/2010, and a Research Scholar at the University of Colorado Denver, USA, in 2017. He is an Associate Professor at Federal University of São Paulo, Brazil.