Overview
- Gives a thorough and systematic introduction to recent research results
- Presents recent research on Decision-Making Analyses With Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations
- Can be used as a reference for researchers and practitioners working in fuzzy mathematics, operations research, information science, management science, engineering, etc
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 409)
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Table of contents (9 chapters)
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Front Matter
Keywords
About this book
The book introduces readers to some of the latest advances in and approaches to decision-making methods based on thermodynamic characters and hesitant fuzzy linguistic preference relations. By investigating the decision-making methods with thermodynamic parameters based on different information representatives, the book offers readers a novel perspective for solving problems under uncertainty. By exploring the consistency and consensus of hesitant fuzzy linguistic preference relations, the book gives readers efficient ways for preference analysis under uncertainty, chiefly intended for researchers and practitioners working in operations research, multi-attribute decision making, preference analysis, etc. The book can also be used as supplementary material for postgraduate and senior-year undergraduate students of the relevant professional institutions.
Authors and Affiliations
Bibliographic Information
Book Title: Decision-Making Analyses with Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations
Authors: Peijia Ren, Zeshui Xu
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-030-73253-0
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-73252-3Published: 31 May 2021
Softcover ISBN: 978-3-030-73255-4Published: 01 June 2022
eBook ISBN: 978-3-030-73253-0Published: 30 May 2021
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XII, 130
Number of Illustrations: 10 b/w illustrations, 19 illustrations in colour
Topics: Computational Intelligence, Operations Research/Decision Theory, Artificial Intelligence