Flexible and Generalized Uncertainty Optimization

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.

Discusses how to analyze mathematically imprecise, uncertain, fuzzy information Shows how to construct input data for use in flexible and generalized uncertainty optimization problems Second edition enriched with more examples and a chapter on interval multi-objective mini-max regret theory

Autorentext

Masoud Asadi-Zeydabadi serves as professor in the Department of Physics, University of Colorado Denver. Masoud Asadi received a Ph.D. in physics from University of Colorado Boulder. His research interests include nonlinear dynamics and chaos, physics education, biological modeling, and medical physics. Marco Breda is currently the director of the Advanced Analytics and AI in the division of Data and Analytics Center of Excellence at Engineering Ingegneria Informatica. He works with machine learning paradigms for designing and building complex analytical systems for public and private industrial companies in various ICT sectors. He has worked with various IT companies including Ericsson Telecamunicazione's research division and TIM gathering more than 20 years' experience in data warehousing, business intelligence, and data mining projects. He is also an associate researcher at the Semeion Research Center in Rome, Italy, where he conducts basic research in artificialintelligence. He has published over 30 papers and teaches data warehousing and advanced analytics at the "Enrico della Valle" School since 2010. He received an M.Sc. in electronic engineering from the "Sapienza" University of Rome in 1989. Massimo Buscema is the director of Semeion Research Center in Rome, Italy, and is currently a full professor adjoint at the Department of Mathematical and Statistical Sciences, University of Colorado (Denver, USA). He has published more than 300 scientific papers in the field of AI, about 8 scientific books, and 28 international patents. Massimo does research in algorithms, artificial intelligence, and artificial neural networks. His current projects include EEG in autism diagnosis, geographic profiling, artificial neural networks working with many data sets simultaneously, deep learning, and data matrix theory. Francesca Della Torre is a research associate at the Semeion Research Center in Rome, Italy. She receivedher Master of Science in mathematics from the University of Rome "Tor Vergata" in 2016. Her skills and expertise are in the areas of business intelligence, data science, advanced machine learning, deep learning, computer vision, evolutionary algorithms, artificial intelligence, unsupervised learning, and their respective applications. Weldon Lodwick is a professor in the Department of Mathematical and Statistical Sciences, University of Colorado Denver. His areas of expertise include computational geographical information systems, uncertainty theory, fuzzy set theory, optimization, and applications especially in the areas of medicine. Giulia Massini earned a Laurea in Sociology, University of Rome, "La Sapienza," in 1978. She was one of the founding members and deputy director of Semeion Research Center in Rome, Italy. She has been working as a computer programmer and data scientist, contributing to several publications, both theoretical and applicative. Her research focuses on graph theory, creation of new algorithms in neural networks, and adaptive artificial systems. Francis Newman is a professor emeritus at University of Colorado Denver and an adjoint associate professor at the department of Mathematical and Statistical Sciences at the same university. He served as a head radiation physicist at the University of Colorado Denver Anschutz Medical Campus (University of Colorado Hospital). His skills and expertise are in the areas of radiation dosimetry, radiation therapy physics, radiation oncology, medical radiation physics, radiotherapy physics, radiotherapy, dosimetry, IMRT, artificial neural networks, and optimization especially applied to medicine. Riccardo Petritoli is an associated researcher at Semeion Research Center in Rome, Italy. With over 20 years of experience in machine learning and computer intelligence focused on the design, implementation, and application of analytic adaptive systems, he is also currently serving as a data scientist and customer engagement manager in

Inhalt
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.- References.