WASPAS: Weighted Aggregated Sum Product Assessment

Overview

Convex combination (SAW + WPM). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Convex combination (SAW + WPM)

Limitations

• •Rank reversal known on alternative-set changes (ref: general MCDM literature)
• •Assumes: Criteria preferences are independent (no synergistic interactions)
• •Assumes: Compensation is acceptable: high score on one criterion can offset low on another
• •Assumes: Decision matrix is complete (no missing values)

Assumptions to verify

• •Criteria preferences are independent (no synergistic interactions)
• •Compensation is acceptable: high score on one criterion can offset low on another
• •Decision matrix is complete (no missing values)

When not to use

• •Criteria strongly correlated → consider DEMATEL/ANP for interdependence
• •Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)

Edge cases

• •See F.steps and D.parameters for WASPAS-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'WASPAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'WASPAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'WASPAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: WASPAS'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: WASPAS'yi 'Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE)' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Step 1: Linear normalisation (max for benefit, min/x for cost). Formül: \bar{x}_{ij} = \begin{cases} x_{ij}/\max_{i} x_{ij} & j\in J^{+} \\ \min_{i} x_{ij}/x_{ij} & j\in J^{-} \end{cases} Anchor: Zavadskas 2012, p.4 Eq.(1)
2. 2.Adım 2 (F2): Step 2: Weighted Sum Model component Q^{(1)}_i. Formül: Q^{(1)}_{i} = \sum_{j=1}^{n} w_{j}\,\bar{x}_{ij} Anchor: Zavadskas 2012, p.4 Eq.(2)
3. 3.Adım 3 (F3): Step 3: Weighted Product Model component Q^{(2)}_i. Formül: Q^{(2)}_{i} = \prod_{j=1}^{n} (\bar{x}_{ij})^{w_{j}} Anchor: Zavadskas 2012, p.4 Eq.(3)
4. 4.Adım 4 (F4): Step 4: Joint WASPAS aggregation with λ∈[0,1] and descending ranking. Formül: Q_{i} = \lambda\,Q^{(1)}_{i} + (1-\lambda)\,Q^{(2)}_{i} Anchor: Zavadskas 2012, p.4 Eq.(4)

Commonly paired with

• •AHP + WASPAS (high)
• •BWM + WASPAS (high)
• •ENTROPY + WASPAS (high)
• •CRITIC + WASPAS (high)
• •SWARA + WASPAS (high)

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