created in this way. By using multidistances a mea-
sure of homogeneity of similarity of the different cri-
teria of each project to the fuzzy positive ideal solu-
tion was calculated. This was done to include infor-
mation about the consistency of the level of goodness
of projects (by the selected criteria). This informa-
tion was included in the closeness coefﬁcient that was
used in the ranking of the projects. The ﬁnal ranking
thus includes information about the goodness of each
project (as ranked by TOPSIS) and about the ”stabil-
ity” of the level of goodness of each of the criteria of
each project. The top ﬁve projects from the numerical
example were found to be 15, 5, 18, 19, and 20. No-
table from the results is that projects 15 and 5 were
always top 2 choices, but project 20 varied between
rankings 3 to 7 so that with lower values of orness
ranking was lower and after orness value 0.6 it was
always the third best choice. Forming a fuzzy number
from different rankings allows one to include differ-
ent points of view and creating an intelligent overall
ranking. Furthermore, more relevant information is
carried along in the analysis, until the ranking stage,
enabling the ranking to take more things into consid-
eration and thus being based on a more holistic view
of the problem.
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