Flexible and generalized uncertainty optimization : theory and approaches /

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Bibliographic Details
Author / Creator:Lodwick, Weldon A., author.
Edition:Second edition.
Imprint:Cham, Switzerland : Springer, [2021]
Description:1 online resource (ix, 193 pages) : illustrations (some color).
Language:English
Series:Studies in computational intelligence, 1860-949X ; volume 696
Studies in computational intelligence ; v. 696.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12610929
Hidden Bibliographic Details
Other authors / contributors:Salles-Neto, Luiz L., author.
ISBN:9783030611804
3030611809
9783030611798
3030611795
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed March 3, 2021).
Summary: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.
Other form:Printed edition: 9783030611798
Standard no.:10.1007/978-3-030-61180-4