Since real-world data is often inaccurate and working with fuzzy data and Z-numbers are
very important and necessary, in the real world we need to rank and compare data. In
this paper, we introduce a new method for ranking Z-numbers. This ranking algorithm is
based on centroid point.
We evaluate distance between centroid point, and based on this distance, we rank the
Z-numbers.
We use this method in two practical examples. First in ranking the return on assets of
Tehran stock exchange, and second, in ranking of factors affecting the productivity of
tourism security.
The advantage of this method over conventional fuzzy methods is considering uncertainty,
and allocating credit in the opinion of experts to estimate fuzzy parameters.