Pattern Recognition Letters
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Prioritized aggregation operators
International Journal of Approximate Reasoning
Fuzzy Optimization and Decision Making
Information Sciences: an International Journal
Choquet integrals of weighted intuitionistic fuzzy information
Information Sciences: an International Journal
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
The orness measures for two compound quasi-arithmetic mean aggregation operators
International Journal of Approximate Reasoning
Parameterized OWA operator weights: An extreme point approach
International Journal of Approximate Reasoning
Parametric aggregation in ordered weighted averaging
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Directed graph-based multi-agent coalitional decision making
Knowledge-Based Systems
Hi-index | 0.00 |
In lots of practical multi-criteria decision making (MCDM) problems, there exist various and changeable relations among the criteria which cannot be handled well by means of the existing methods. Considering that graphic or netlike structures can be used to describe the relationships among several individuals, we first introduce the graphic structure into MCDM and formalize the relations among criteria. Then, we develop a new tool, called graph-based multi-agent decision making (GMADM) model, to deal with a kind of MCDM problems with the interrelated criteria. In the model, the graphic structure is paid sufficient attention to in two main aspects: (1) how the graphic structure has influence on the benefits of agents (or the criteria values); and (2) the relation between the graphic structure and the importance weights of agents (criteria). In this case, we can select the best plan(s) (or alternative(s)) according to the overall benefits (the overall criteria values) resulting from the model. Moreover, a fuzzy graph-based multi-agent decision making (FGMADM) method is developed to solve a common kind of situations where the graphic structure of agents is uncertain (confidential or false). Three examples are used to illustrate the feasibility of these two developed methods.