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Decision-making analyses with thermo...
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Ren, Peijia.
Decision-making analyses with thermodynamic parameters and hesitant fuzzy linguistic preference relations
Record Type:
Electronic resources : Monograph/item
Title/Author:
Decision-making analyses with thermodynamic parameters and hesitant fuzzy linguistic preference relationsby Peijia Ren, Zeshui Xu.
Author:
Ren, Peijia.
other author:
Xu, Zeshui.
Published:
Cham :Springer International Publishing :2021.
Description:
xii, 130 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Decision making.
Online resource:
https://doi.org/10.1007/978-3-030-73253-0
ISBN:
9783030732530$q(electronic bk.)
Decision-making analyses with thermodynamic parameters and hesitant fuzzy linguistic preference relations
Ren, Peijia.
Decision-making analyses with thermodynamic parameters and hesitant fuzzy linguistic preference relations
[electronic resource] /by Peijia Ren, Zeshui Xu. - Cham :Springer International Publishing :2021. - xii, 130 p. :ill., digital ;24 cm. - Studies in fuzziness and soft computing,v.4091434-9922 ;. - Studies in fuzziness and soft computing ;v.273..
: Chapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3 A Thermodynamic Method For Intuitionistic Fuzzy Decision Making -- Chapter 4. A Thermodynamic Method For Hesitant Fuzzy Decision Making Based On Prospect Theory -- Chapter 5: A Thermodynamic Method For Heterogeneous Decision Making Based On Confidence Level -- Chapter 6: A Programming Model For Hesitant Fuzzy Linguistic Preference Relation.
The book introduces readers to some of the latest advances in and approaches to decision-making methods based on thermodynamic characters and hesitant fuzzy linguistic preference relations. By investigating the decision-making methods with thermodynamic parameters based on different information representatives, the book offers readers a novel perspective for solving problems under uncertainty. By exploring the consistency and consensus of hesitant fuzzy linguistic preference relations, the book gives readers efficient ways for preference analysis under uncertainty, chiefly intended for researchers and practitioners working in operations research, multi-attribute decision making, preference analysis, etc. The book can also be used as supplementary material for postgraduate and senior-year undergraduate students of the relevant professional institutions.
ISBN: 9783030732530$q(electronic bk.)
Standard No.: 10.1007/978-3-030-73253-0doiSubjects--Topical Terms:
183849
Decision making.
LC Class. No.: QA279.4 / .R45 2021
Dewey Class. No.: 519.542
Decision-making analyses with thermodynamic parameters and hesitant fuzzy linguistic preference relations
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: Chapter 1: Introduction -- Chapter 2: Literature Review -- Chapter 3 A Thermodynamic Method For Intuitionistic Fuzzy Decision Making -- Chapter 4. A Thermodynamic Method For Hesitant Fuzzy Decision Making Based On Prospect Theory -- Chapter 5: A Thermodynamic Method For Heterogeneous Decision Making Based On Confidence Level -- Chapter 6: A Programming Model For Hesitant Fuzzy Linguistic Preference Relation.
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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.
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Intelligent Technologies and Robotics (SpringerNature-42732)
based on 0 review(s)
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EB QA279.4 .R393 2021 2021
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https://doi.org/10.1007/978-3-030-73253-0
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