FUZZY-SWARA - Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF-SWARA)

Overview

FUZZY-SWARA returns crisp criterion weights (Σ w_j = 1, w_j ≥ 0). The DM ranks criteria most→least important, then states a triangular-fuzzy comparative importance s̃_j for each criterion vs its predecessor (s̃_1 = (0,0,0)). The fuzzy step-ratios are propagated by TFN division and defuzzified by the graded mean. Compared with crisp SWARA it lets experts express uncertainty in each comparative judgement.

Output

Weight, higher is better

Data

Fuzzy (TFN), expert input required

Weights

Derived internally, no weight source needed

Size

2+ alternatives, 3-12 criteria works best

Used for

Hierarchical weighting, expert-driven

Look elsewhere when

Assumptions to verify

• Domain experts available
• Experts can express consistent comparisons

Edge cases and pitfalls

• s̃_1 must be (0,0,0): the most important criterion has no predecessor.

• Each s̃_j must satisfy l ≤ m ≤ u.

Works with

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