WEDBA: Weighted Euclidean Distance Based Approach

Overview

Distance-based with vector normalisation. Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Distance-based with vector normalisation

Limitations

• •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 WEDBA-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'WEDBA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'WEDBA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'WEDBA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: WEDBA'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: WEDBA'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: Decision matrix X = [x_ij]_{m×n}. Formül: X = \big[x_{ij}\big]_{m\times n} Anchor: Dadelo 2014, Eq.(1)
2. 2.Adım 2 (F2): Step 2: Vector normalisation r_ij. Formül: r_{ij} = \dfrac{x_{ij}}{\sqrt{\sum_{i=1}^{m} x_{ij}^{2}}} Anchor: Dadelo 2014, Eq.(2)
3. 3.Adım 3 (F3): Step 3: Weighted matrix v_ij = w_j r_ij. Formül: v_{ij} = w_{j}\,r_{ij} Anchor: Dadelo 2014, Eq.(3)
4. 4.Adım 4 (F4): Step 4: Best v_j^+ and worst v_j^- per criterion direction. Formül: v_{j}^{+} = \max_{i} v_{ij}\ (J^{+}),\ \min\ (J^{-});\quad v_{j}^{-}\text{ aksi} Anchor: Dadelo 2014, Eq.(4)
5. 5.Adım 5 (F5): Step 5: Weighted Euclidean D_i^+ and D_i^-. Formül: D_{i}^{+} = \sqrt{\sum_{j}(v_{ij}-v_{j}^{+})^{2}},\quad D_{i}^{-} = \sqrt{\sum_{j}(v_{ij}-v_{j}^{-})^{2}} Anchor: Dadelo 2014, Eq.(5)
6. 6.Adım 6 (F6): Step 6: Z_i = D_i^- / (D_i^+ + D_i^-); descending ranking. Formül: Z_{i} = \dfrac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}},\quad 0\le Z_{i}\le 1 Anchor: Dadelo 2014, Eq.(6)

Commonly paired with

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

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