Fang Liu 0017

dblp:67/5807-17 · DBLP profile ↗
← Back
8ranked-venue papers in the field
6as first author
4since 2021 · last 2026
0000-0003-3691-6902ORCID · conflict

Domains — venue-derived; a paper can count in several

Knowledge Engineering, Semantic Web & Information Systems · 7 (5 first)Other / Interdisciplinary · 1 (1 first)
YearPublicationVenuePosition
2026 A collective-intelligence-driven parametric model for measuring and improving group performance
Fang Liu 0017, Da-Hai Zhou, Xin-Yu Wei
Inf. Sci.1
2024 Algebraic analysis of pairwise comparisons with non-reciprocal property
Yuan-Kai Hu, Fang Liu 0017, Yu-Ru Cai
Inf. Sci.2
2024 Building consensus in multi-attribute group decision making under a prospect theory-driven feedback adjustment mechanism
Fang Liu 0017, Shi-Shan Wang
Inf. Sci.1
2021 The breaking of additively reciprocal property of fuzzy preference relations and its implication to decision making under uncertainty
Fang Liu 0017, Qi-Rui You, Yuan-Kai Hu, Weiguo Zhang 0002
Inf. Sci.1
2018 A decision-making model based on interval additive reciprocal matrices with additive approximation-consistency
Fang Liu 0017, Ya-Nan Peng, Qin Yu 0002
Inf. Sci.1
2018 An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information
Tong Wu 0004, Xinwang Liu 0001, Fang Liu 0017
Inf. Sci.3
2015 A Modified TOPSIS Method for Obtaining the Associated Weights of the OWA-Type Operators
abstract
The ordered weighted averaging (OWA) operator proposed by Yager and its some extensions have been extensively utilized to perform the mean-type aggregation of individual preference relations in group decision making. An important issue in the theory of the OWA-type operators is the determination of the associated weights. In the present paper, the technique for order preference by similarity to ideal solution is modified to propose a new method for generating the associated weights under the environment of a group of additive reciprocal matrices. First, a consistent additive reciprocal matrix is obtained from each additive reciprocal matrix. It is further used to generate an ideal additive reciprocal matrix by using the arithmetic averaging operator. Then, a similarity degree between every additive reciprocal matrix and the ideal one is defined. Based on the similarity degree, the associated weights are determined by making use of an exponential function and they show that more importance is given to that with more similarity degree. Finally, a numerical example is carried out to illustrate the given method.
Fang Liu 0017, Yu-Fan Shang, Li-Hua Pan
Int. J. Intell. Syst.1
2012 A new method of obtaining the priority weights from an interval fuzzy preference relation
Fang Liu 0017, Weiguo Zhang 0002, Jun-Hui Fu
Inf. Sci.1