A heuristic nonlinear operator for the aggregation of incomplete judgment matrices in group decision making

The weighted-average operator and ordered-weighted-average operators are typically used in group decision making (GDM) problems to aggregate individual expert opinions to a collective opinion. However, the existing aggregation operators pay more attentions on the determination of the weights, and ne...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2015-12, Vol.26 (6), p.1253-1266
Main Authors: Jiao, Bo, Zhou, Ying, Du, Jing, Huang, Cheng-dong, Wang, Jun-hu, Li, Bo
Format: Article
Language:English
Subjects:
Agglomeration
Business and Management
Consistency
Control
Decision making
Groups
Heuristic
Judgments
Linguistics
Machines
Manufacturing
Mathematical models
Mechatronics
Operators
Optimization
Processes
Production
Quadratic programming
Robotics
Similarity
Studies
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Summary:The weighted-average operator and ordered-weighted-average operators are typically used in group decision making (GDM) problems to aggregate individual expert opinions to a collective opinion. However, the existing aggregation operators pay more attentions on the determination of the weights, and neglect the information about the relationship between the values being fused. In this paper, we develop a heuristic-nonlinear-aggregation (HNA) operator based on two metrics of similarity and consistency for the GDM based on incomplete judgment matrices. The similarity and consistency respectively measure the differences between a collective matrix and two optimum matrices, i.e. the optimum similarity matrix and the optimum consistency matrix, which can be calculated by quadratic programming models and the relationship between the values being fused. The validity and practicability of the HNA operator are illustrated by numerical examples.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-013-0854-7