International Journal of Computational Intelligence Systems

Volume 12, Issue 2, 2019, Pages 1029 - 1046

Revisiting the Role of Hesitant Multiplicative Preference Relations in Group Decision Making With Novel Consistency Improving and Consensus Reaching Processes

Authors
Rui Wang1, 2, Bin Shuai1, 2, *, Zhen-Song Chen3, *, Kwai-Sang Chin4, Jiang-Hong Zhu1, 2
1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 610031, China
2National United Engineering Laboratory of Intergrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, 610031, China
3School of Civil Engineering, Wuhan University, Wuhan, 430072, China
4Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong, 999077, China
*Corresponding authors. Emails: shuaibin@home.swjtu.edu.cn; zschen@whu.edu.cn
Corresponding Authors
Bin Shuai, Zhen-Song Chen
Received 3 July 2019, Accepted 20 August 2019, Available Online 26 September 2019.
DOI
10.2991/ijcis.d.190823.001How to use a DOI?
Keywords
Group decision making; Hesitant multiplicative preference relation; Consistency improving process; Consensus reaching process; Mathematical programming model
Abstract

In recent years, hesitant multiplicative preference relation (HMPR) has been a powerful means to represent the evaluation information of decision makers during the pairwise comparison concerning alternatives. As the important parts of group decision making (GDM) issues with HMPRs, the consistency improving and consensus reaching processes have been researched by many scholars; however, the existing approaches present several limitations, including defining the consistency index depend on the other HMPR instead of the original HMPR, improving the consistency and consensus levels of HMPRs independently, and that the high computational complexity of the existing iterative algorithms. To overcome these drawbacks, this paper proposes a mathematical programming model to improve the consistency and consensus levels of HMPRs, simultaneously. First, a consistency index according to multiplicative consistency is put forward to compute the consistency degree of normalized HMPR (NHMPR) after the normalization procedure. Second, a programming model by minimizing the difference between the original NHMPR and the revised NHMPR is developed to obtain the acceptably consistent NHMPR; then, a consistency-based method is constructed to solve the decision making problems with an HMPR. Third, considering the GDM issues, a consistency- and consensus-based programming model is established to obtain the acceptably consistent and consensus NHMPRs, in which the original evaluation information can remain as much as possible. Fourth, the normalized hesitant multiplicative weighted geometric operator is introduced to fuse the revised NHMPRs and an algorithm for GDM is proposed with novel consistency improving and consensus reaching processes. Finally, two numerical examples are applied to show the practicality and advantages of the proposed approaches.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1029 - 1046
Publication Date
2019/09/26
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.190823.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Rui Wang
AU  - Bin Shuai
AU  - Zhen-Song Chen
AU  - Kwai-Sang Chin
AU  - Jiang-Hong Zhu
PY  - 2019
DA  - 2019/09/26
TI  - Revisiting the Role of Hesitant Multiplicative Preference Relations in Group Decision Making With Novel Consistency Improving and Consensus Reaching Processes
JO  - International Journal of Computational Intelligence Systems
SP  - 1029
EP  - 1046
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.190823.001
DO  - 10.2991/ijcis.d.190823.001
ID  - Wang2019
ER  -