Автор: Young-Jou Lai; Ching-Lai Hwang Название: Fuzzy Multiple Objective Decision Making ISBN: 3540575952 ISBN-13(EAN): 9783540575955 Издательство: Springer Рейтинг: Цена: 20263.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Methods of crisp, fuzzy and possibilistic multiple objective decision-making are systematically reviewed and classified in this volume. The reader is provided with a survey of existing methods and their application to the analysis of fuzzy programming problems.
Автор: G?nter Fandel; T. Hanne; Tomas Gal Название: Multiple Criteria Decision Making ISBN: 3540620974 ISBN-13(EAN): 9783540620976 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume presents recent results in theory, methodology and applications of multiple criteria decision-making (MCDM). All facets of MCDM are included, like fuzzy models, nonlinear convex problems, simulated annealing, integer programming models and goal programming.
Автор: Ignacy Kaliszewski Название: Soft Computing for Complex Multiple Criteria Decision Making ISBN: 1441940189 ISBN-13(EAN): 9781441940186 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book concentrates on providing technical tools to make the user of Multiple Criteria Decision Making (MCDM) methodologies independent of bulky optimization computations. These bulky computations have been a necessary, but limiting, characteristic of interactive MCDM methodologies and algorithms.
Автор: F.P. Hwang; Shu-Jen Chen; Ching-Lai Hwang Название: Fuzzy Multiple Attribute Decision Making ISBN: 3540549986 ISBN-13(EAN): 9783540549987 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. The basic concepts and algorithms from the classical MADM methods have been used in the development of the fuzzy MADM methods.
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