书目名称 | Flexible and Generalized Uncertainty Optimization |
副标题 | Theory and Approache |
编辑 | Weldon A. Lodwick,Luiz L. Salles-Neto |
视频video | |
概述 | 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 e |
丛书名称 | Studies in Computational Intelligence |
图书封面 |  |
描述 | .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.. |
出版日期 | Textbook 2021Latest edition |
关键词 | Fuzzy Intervals; Constraint Sets; Possibility Intervals; Necessity Measures; Kolmogorov-Smirnov Bounds; C |
版次 | 2 |
doi | https://doi.org/10.1007/978-3-030-61180-4 |
isbn_softcover | 978-3-030-61182-8 |
isbn_ebook | 978-3-030-61180-4Series ISSN 1860-949X Series E-ISSN 1860-9503 |
issn_series | 1860-949X |
copyright | Springer Nature Switzerland AG 2021 |