MABAC: Multi-Attributive Border Approximation area Comparison

Overview

Border approximation area (distance from BAA). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Border approximation area (distance from BAA)

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

Common pitfalls

• •Hatalı: 'MABAC bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'MABAC bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'MABAC bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: MABAC'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: MABAC'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 max-min normalisation per criterion type. Formül: n_{ij} = \begin{cases}\dfrac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}} & j\in J^{+}\\\dfrac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}} & j\in J^{-}\end{cases} Anchor: Pamucar-Cirovic 2015, p.3018 Eq.(2)
2. 2.Adım 2 (F2): Step 2: Weighted normalised matrix v_ij = w_j · (n_ij + 1). Formül: v_{ij} = w_{j}\,(n_{ij}+1) Anchor: Pamucar-Cirovic 2015, p.3018 Eq.(3)
3. 3.Adım 3 (F3): Step 3: Border Approximation Area (BAA) per criterion: geometric mean. Formül: g_{j} = \Big(\prod_{i=1}^{m} v_{ij}\Big)^{1/m} Anchor: Pamucar-Cirovic 2015, p.3019 Eq.(4)
4. 4.Adım 4 (F4): Step 4: Distance from BAA: q_ij = v_ij − g_j. Formül: q_{ij} = v_{ij} - g_{j} Anchor: Pamucar-Cirovic 2015, p.3019 Eq.(5)
5. 5.Adım 5 (F5): Step 5: Alternative score S_i = Σ q_ij and descending ranking. Formül: S_{i} = \sum_{j=1}^{n} q_{ij} Anchor: Pamucar-Cirovic 2015, p.3019 Eq.(6)

Commonly paired with

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

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