Methods of ranking for aggregated fuzzy numbers from interval-valued
  data
  This paper primarily presents two methods of ranking aggregated fuzzy numbers
from intervals using the Interval Agreement Approach (IAA). The two proposed
ranking methods within this study contain the combination and application of
previously proposed similarity measures, along with attributes novel to that of
aggregated fuzzy numbers from interval-valued data. The shortcomings of
previous measures, along with the improvements of the proposed methods, are
illustrated using both a synthetic and real-world application. The real-world
application regards the Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS) algorithm, modified to include both the previous and
newly proposed methods.
