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Fuzzy multiple objective decision making / Gwo-Hshiung Tzeng and Jih-Jeng Huang.

By: Tzeng, Gwo-Hshiung.
Contributor(s): Huang, Jih-Jeng.
Material type: TextTextPublisher: Boca Raton : CRC Press, 2014Description: xiv, 308 p. : illustrations ; 24 cm.ISBN: 9781466554610 (hardback : acidfree paper).Subject(s): Multiple criteria decision making | Fuzzy sets | Management science | BUSINESS & ECONOMICS / Operations Research | MATHEMATICS / Applied | TECHNOLOGY & ENGINEERING / Engineering (General)DDC classification: 658.4033
Contents:
1. Introduction-- Section 1. Concepts and theory of multi-objective decision making -- 2. Multi-objective evolutionary algorithms-- 3. Goal programming-- 4.Compromise solution and TOPSIS-- 5. De novo programming and changeable parameters-- 6. Multi-stage Programming-- 7. Multi-level multi-objective programming-- 8. Data envelopment analysis-- section 2. Applications of multi-objective decision making-- 9. Motivation and resource allocation for stratgic alliances through the de nove perspective-- 10. Choosing best alliance partners and allocating optimal alliance resources using Fuzzy multi-objective dummy programming model-- 11. Multiple-objective planning for supply chain production and distribution model: Bicycle manufacturer-- 12. Fuzzy interdependent multi-objective programming-- 13. Novel algorithm for uncertain portfolio selection-- 14. Multi-objective optimal planning for designing relief delivery systems-- 15. Comparative productivity efficiency for global telecoms-- 16. Fuzzy multiple objective programming in interval piecewise regression model-- Bibliography-- Notes-- Index.
Summary: "Preface Operations research has been adapted by management science scholoars to manage realistic problems for a long time. Among these methods, mathematical programming models play a key role in optimizing a system. However, traditional mathematical programming focuses on single-objective optimization rather than multi-objective optimization as we encounter in real situation. Hence, the concept of multi-objective programming was proposed by Kuhn, Tucker and Koopmans in 1951 and since then became the main-stream of mathematical programming. Multi-objective programming (MOP) can be considered as the natural extension of single-objective programming by simultaneously optimizing multi-objectives in mathematical programming models. However, the optimization of multi-objectives triggers the issue of the Pareto solutions and complicates the derived answer. In addition, more scholars incorporate the concepts of fuzzy sets and evolutionary algorithms to multi-objective programming models and enrich the field of multi-objective decision making (MODM). The content of this book is divided into two parts: methodologies and applications. In the first part, we introduced most popular methods which are used to calculate the solution of MOP in the field of MODM. Furthermore, we included three new topics of MODM: multi-objective evolutionary algorithms (MOEA), expanding De Novo programming to changeable spaces, including decision space and objective space, and network data envelopment analysis (NDEA) in this book. In the application part, we proposed different kind of practical applications in MODM. These applications can provide readers the insights for better understanding the MODM with depth. "--
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
658.4033 T998 (Browse shelf) Available 135744
Total holds: 0

Includes bibliographical references and index.

1. Introduction--
Section 1. Concepts and theory of multi-objective decision making --
2. Multi-objective evolutionary algorithms--
3. Goal programming--
4.Compromise solution and TOPSIS--
5. De novo programming and changeable parameters--
6. Multi-stage Programming--
7. Multi-level multi-objective programming--
8. Data envelopment analysis--

section 2. Applications of multi-objective decision making--
9. Motivation and resource allocation for stratgic alliances through the de nove perspective--
10. Choosing best alliance partners and allocating optimal alliance resources using Fuzzy multi-objective dummy programming model--
11. Multiple-objective planning for supply chain production and distribution model: Bicycle manufacturer--
12. Fuzzy interdependent multi-objective programming--
13. Novel algorithm for uncertain portfolio selection--
14. Multi-objective optimal planning for designing relief delivery systems--
15. Comparative productivity efficiency for global telecoms--
16. Fuzzy multiple objective programming in interval piecewise regression model--

Bibliography--
Notes--
Index.


"Preface Operations research has been adapted by management science scholoars to manage realistic problems for a long time. Among these methods, mathematical programming models play a key role in optimizing a system. However, traditional mathematical programming focuses on single-objective optimization rather than multi-objective optimization as we encounter in real situation. Hence, the concept of multi-objective programming was proposed by Kuhn, Tucker and Koopmans in 1951 and since then became the main-stream of mathematical programming. Multi-objective programming (MOP) can be considered as the natural extension of single-objective programming by simultaneously optimizing multi-objectives in mathematical programming models. However, the optimization of multi-objectives triggers the issue of the Pareto solutions and complicates the derived answer. In addition, more scholars incorporate the concepts of fuzzy sets and evolutionary algorithms to multi-objective programming models and enrich the field of multi-objective decision making (MODM). The content of this book is divided into two parts: methodologies and applications. In the first part, we introduced most popular methods which are used to calculate the solution of MOP in the field of MODM. Furthermore, we included three new topics of MODM: multi-objective evolutionary algorithms (MOEA), expanding De Novo programming to changeable spaces, including decision space and objective space, and network data envelopment analysis (NDEA) in this book. In the application part, we proposed different kind of practical applications in MODM. These applications can provide readers the insights for better understanding the MODM with depth. "--

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