Illustrative example: a representative scenario, not a customer story
Renewable Energy Technology Assessment
Energy consultancy evaluates solar, wind, biomass, geothermal, and hybrid options under uncertainty using triangular fuzzy numbers from 5 expert panels.
The decision problem
An energy team compares five generation options for one site: solar, wind, biomass, geothermal and a hybrid configuration. The engineering figures are known within a range rather than exactly, and the expert panel scores grid compatibility and land use on a verbal scale rather than in numbers. Forcing those verbal scores into single crisp values throws away the very thing the panel was hesitant about.
What the decision matrix looks like
The typical shape of a study like this. Your own matrix can be larger or smaller; nothing here is fixed.
5
Alternatives
5
Criteria
Verbal and fuzzy expert assessment
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Levelised cost of electricity |
| C2 | Capacity factor |
| C3 | Payback period |
| C4 | Grid compatibility |
| C5 | Land use |
Which method, and why
Fuzzy TOPSIS carries triangular fuzzy numbers through the whole computation, so a verbal score stays an interval until the final ranking. BWM produces consistent weights from far fewer comparisons than a full pairwise matrix, which matters when the panel meets once. EDAS ranks by distance from the average solution and gives a second reading built on a different reference point.
What you get out
Ranking and scores
Every alternative with its score, its rank and the intermediate matrices that produced them.
Weight sensitivity
One criterion weight moves at a time, so you see exactly where the leader changes.
Method agreement
When more than one ranking method is run, the orders are compared with Spearman rho and Kendall W.
Report and citation
PDF, DOCX and XLSX output carrying the seminal source of every method used.
Run this flow on your own data
Load the matrix, choose the weighting and ranking methods, read the sensitivity.