1.

Record Nr.

UNINA9910254170903321

Autore

Lodwick Weldon A

Titolo

Flexible and Generalized Uncertainty Optimization : Theory and Methods / / by Weldon A. Lodwick, Phantipa Thipwiwatpotjana

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

ISBN

3-319-51107-6

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (X, 190 p. 32 illus., 16 illus. in color.)

Collana

Studies in Computational Intelligence, , 1860-949X ; ; 696

Disciplina

519.3

Soggetti

Computational intelligence

Operations research

Management science

Probabilities

Computational Intelligence

Operations Research, Management Science

Probability Theory and Stochastic Processes

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

1 An Introduction to Generalized Uncertainty Optimization -- 2 Generalized Uncertainty Theory: A Language for Information Deficiency -- 3 The Construction of Flexible and Generalized Uncertainty Optimization Input Data -- 4 An Overview of Flexible and Generalized Uncertainty Optimization -- 5 Flexible Optimization -- 6 Generalized Uncertainty Optimization -- References. .

Sommario/riassunto

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. .