EDAS: Evaluation Based on Distance from Average Solution

Overview

Distance from average solution. Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Distance from average solution

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 EDAS-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'EDAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'EDAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'EDAS bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: EDAS'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: EDAS'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: Compute the Average Solution (AV) per criterion: AV_j = (1/m) Σ_i x_ij. Formül: AV_{j} = \dfrac{1}{m} \sum_{i=1}^{m} x_{ij} Anchor: Keshavarz-Ghorabaee 2015, p.439 Eq.(3)
2. 2.Adım 2 (F2): Step 2: Positive Distance from Average (PDA): direction-aware deviation above AV. Formül: PDA_{ij} = \begin{cases} \dfrac{\max(0,\, x_{ij}-AV_{j})}{AV_{j}} & j\in J^{+} \\ \dfrac{\max(0,\, AV_{j}-x_{ij})}{AV_{j}} & j\in J^{-} \end{cases} Anchor: Keshavarz-Ghorabaee 2015, p.439 Eq.(4)
3. 3.Adım 3 (F3): Step 3: Negative Distance from Average (NDA): direction-aware deviation below AV. Formül: NDA_{ij} = \begin{cases} \dfrac{\max(0,\, AV_{j}-x_{ij})}{AV_{j}} & j\in J^{+} \\ \dfrac{\max(0,\, x_{ij}-AV_{j})}{AV_{j}} & j\in J^{-} \end{cases} Anchor: Keshavarz-Ghorabaee 2015, p.439 Eq.(5)
4. 4.Adım 4 (F4): Step 4: Weighted sums SP_i and SN_i across criteria. Formül: SP_{i} = \sum_{j=1}^{n} w_{j}\,PDA_{ij}, \quad SN_{i} = \sum_{j=1}^{n} w_{j}\,NDA_{ij} Anchor: Keshavarz-Ghorabaee 2015, p.439 Eqs.(6)-(7)
5. 5.Adım 5 (F5): Step 5: Normalize SP, SN by their maxima. Formül: NSP_{i} = \dfrac{SP_{i}}{\max_{k} SP_{k}}, \quad NSN_{i} = 1 - \dfrac{SN_{i}}{\max_{k} SN_{k}} Anchor: Keshavarz-Ghorabaee 2015, p.440 Eqs.(8)-(9)
6. 6.Adım 6 (F6): Step 6: Appraisal Score AS_i and descending ranking. Formül: AS_{i} = \tfrac{1}{2}(NSP_{i} + NSN_{i}),\quad 0 \le AS_{i} \le 1 Anchor: Keshavarz-Ghorabaee 2015, p.440 Eq.(10)

Commonly paired with

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

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