Hesitant fuzzy methods for multiple criteria decision analysis

The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors' latest research, as well as on others' research, providing readers with a complete set of dec...

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Bibliographic Details
Main Authors: Zhang, Xiaolu, (Author), Xu, Zeshui, (Author)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, [2016]
Series: Studies in fuzziness and soft computing ; v. 345.
Subjects:
Fuzzy decision making.
Multiple criteria decision making.
elektronické knihy
electronic books
ISBN: 9783319420011
9783319420004
Physical Description: 1 online resource (xv, 191 pages) : illustrations (some color)

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Table of contents

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035 |a (OCoLC)960642952  |z (OCoLC)962017212 
100 1 |a Zhang, Xiaolu,  |e author. 
245 1 0 |a Hesitant fuzzy methods for multiple criteria decision analysis /  |c Xiaolu Zhang, Zeshui Xu. 
264 1 |a Cham, Switzerland :  |b Springer,  |c [2016] 
264 4 |c ©2017 
300 |a 1 online resource (xv, 191 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Studies in fuzziness and soft computing,  |x 1434-9922 ;  |v volume 345 
504 |a Includes bibliographical references. 
505 0 |a Intro; Preface; Contents; 1 Hesitant Fuzzy Multiple Criteria Decision Analysis Based on TOPSIS; Abstract; 1.1 Hesitant Fuzzy Information; 1.2 Description of the Classical TOPSIS Method; 1.3 The Maximizing Deviation Model-Based Hesitant Fuzzy TOPSIS Approach; 1.3.1 The Maximizing Deviation Model to Determine Weights of Criteria; 1.3.2 The Hesitant Fuzzy TOPSIS Model to Rank the Alternatives; 1.4 Numerical Example and Comparison Analysis; 1.4.1 Problem Description and Decision Making Process; 1.4.2 Comparison Analysis 
505 8 |a 1.5 Extension of the Proposed Method in the Interval-Valued Hesitant Fuzzy Situations1.5.1 Interval-Valued Hesitant Fuzzy Information; 1.5.2 The Maximizing Deviation Model-Based Interval-Valued Hesitant Fuzzy TOPSIS Approach; 1.5.3 Numerical Example with IVHFEs; 1.6 Conclusions; References; 2 Hesitant Fuzzy Multiple Criteria Decision Analysis Based on TODIM; Abstract; 2.1 Description of the Classical TODIM Method; 2.2 Ranking Functions Related to HFEs and IVHFEs; 2.2.1 The Existing Ranking Functions of HFEs; 2.2.2 The Proposed Ranking Functions 
505 8 |a 2.3 The Ranking Functions-Based Hesitant Fuzzy TODIM Approach2.4 Illustration Example Based on the Evaluation Problem of Service Quality; 2.4.1 Description; 2.4.2 Decision Making Model; 2.4.3 Sensitivity Analysis of Parameters; 2.4.4 Comparative Analysis and Discussions; 2.5 Extension of the Developed Approach for Handling the MCGDM Problems with HTrFNs; 2.5.1 The Concept of HTrFN; 2.5.2 Hesitant Trapezoidal Fuzzy TODIM Decision Analysis Method; 2.5.3 Case Illustration; 2.6 Conclusions; References; 3 Hesitant Fuzzy Multiple Criteria Decision Analysis Based on QUALIFLEX; Abstract 
505 8 |a 3.1 The Concept of a Hesitancy Index for HFE3.1.1 The Hesitancy Index; 3.1.2 The Improved Distance Measures for HFEs; 3.1.3 The Comparison Methods Based on Hesitancy Indices for HFEs; 3.2 Hesitant Fuzzy QUALIFLEX Approach with a Signed Distance-Based Ranking Method; 3.3 A Case Study for Green Supplier Selection; 3.3.1 Decision Context; 3.3.2 Illustration of the Proposed Method; 3.3.3 Comparative Analysis and Discussions; 3.4 Extension of the Proposed Method for Heterogeneous Information; 3.5 Conclusions and Future Research Directions; References 
505 8 |a 4 Hesitant Fuzzy Multiple Criteria Decision Analysis Based on LINMAPAbstract; 4.1 Some Basic Concepts; 4.1.1 Description of the Hesitant Fuzzy MCGDM Problems; 4.1.2 Interval-Valued Preference Relations; 4.1.3 Hesitant Fuzzy Preference Relation; 4.1.4 Optimization Problems with Interval-Objective Functions; 4.2 Hesitant Fuzzy LINMAP Group Decision Method with Interval Programming Models; 4.2.1 The Proposed Approach; 4.2.2 Other Generalizations of the Proposed Model; 4.2.3 An Energy Project Selection Problem and the Analysis Process; 4.2.3.1 Description of the Energy Project Selection Problem 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors' latest research, as well as on others' research, providing readers with a complete set of decision making tools, such as hesitant fuzzy TOPSIS, hesitant fuzzy TODIM, hesitant fuzzy LINMAP, hesitant fuzzy QUALIFEX, and the deviation modeling approach with heterogeneous fuzzy information. The main focus is on decision making problems in which the criteria values and/or the weights of criteria are not expressed in crisp numbers but are more suitable to be denoted as hesitant fuzzy elements. The largest part of the book is devoted to new methods recently developed by the authors to solve decision making problems in situations where the available information is vague or hesitant. These methods are presented in detail, together with their application to different type of decision-making problems. All in all, the book represents a valuable reference guide for graduate students and researchers in the both fields of fuzzy logic and decision making. . 
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650 0 |a Multiple criteria decision making. 
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700 1 |a Xu, Zeshui,  |e author. 
776 0 8 |i Print version:  |a Zhang, Xiaolu.  |t Hesitant fuzzy methods for multiple criteria decision analysis.  |d Cham, Switzerland : Springer, [2016]  |z 3319420003  |z 9783319420004  |w (OCoLC)951643024 
830 0 |a Studies in fuzziness and soft computing ;  |v v. 345.  |x 1434-9922 
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