Illustrative example: a representative scenario, not a customer story
Public Transport Route Optimization
Optimize bus route networks balancing coverage, ridership, operating cost, and environmental impact. Neutrosophic sets model conflicting stakeholder opinions while hierarchical criteria structure captures policy priorities.
The decision problem
Nine route configurations are compared for a bus network redesign. The municipality wants coverage, the operator wants cost per kilometre and riders want journey time; these are not different weights on one goal, they are different goals. An evaluator asked to score a route for all three at once answers with genuine ambivalence.
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.
9
Alternatives
5
Criteria
Conflicting stakeholder views
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Population covered |
| C2 | Projected ridership |
| C3 | Operating cost |
| C4 | Journey time |
| C5 | Emissions |
Which method, and why
Neutrosophic EDAS records that ambivalence as separate truth, indeterminacy and falsity degrees rather than collapsing it into one middling number. AHP turns the policy hierarchy into weights and shows, through its consistency ratio, whether the stated priorities hold together. TOPSIS provides a crisp cross-check on the same matrix.
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.