Illustrative example: a representative scenario, not a customer story
Smart City Infrastructure Prioritization
City council prioritizes 10 infrastructure projects using neutrosophic expert evaluations that capture truth, indeterminacy, and falsity of assessments.
The decision problem
A city has ten proposed infrastructure projects and a budget for a few of them. The evaluators are confident about some projects and openly unsure about others, and that hesitation is information: an expert who says the project is probably good but the operating load is unknown should not be recorded like one who is certain. A single score erases the difference.
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.
10
Alternatives
5
Criteria
Expert hesitation: truth, indeterminacy and falsity
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Population served |
| C2 | Capital cost |
| C3 | Operating burden |
| C4 | Carbon impact |
| C5 | Institutional readiness |
Which method, and why
Neutrosophic TOPSIS carries truth, indeterminacy and falsity as separate degrees, so hesitation stays visible until the ranking step. SWARA builds weights from a simple ordering and successive importance ratios, which a small panel can complete in one session. EDAS cross-checks the order against the average solution.
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.