ARTASI: Alternative Ranking Technique based on Adaptive Standardized Intervals

Overview

Two-level standardization + ideal/anti-ideal utility (β-anchored). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Two-level standardization + ideal/anti-ideal utility (β-anchored)

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

Common pitfalls

• •Hatalı: 'ARTASI bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'ARTASI bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'ARTASI bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: ARTASI'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: ARTASI'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 (Eqs 17-18): Adaptive standardized intervals: S_j^max = max_i L_ij + (max_i L_ij)^(1/m); S_j^min = min_i L_ij − (min_i L_ij)^(1/m). Formül: S_{j}^{\max} = \max_{i} L_{ij} + (\max_{i} L_{ij})^{1/m};\quad S_{j}^{\min} = \min_{i} L_{ij} - (\min_{i} L_{ij})^{1/m} Anchor: Kara et al. 2024, Eqs (17)-(18)
2. 2.Adım 2 (F2): Step 2 (Eq 19): First-level standardization: linear rescaling of L_ij to interval [β^l, β^u] using adaptive bounds. Formül: R_{ij} = \dfrac{L_{ij} - S_{j}^{\min}}{S_{j}^{\max} - S_{j}^{\min}}\,(\beta^{u}-\beta^{l}) + \beta^{l} Anchor: Kara et al. 2024, Eq (19)
3. 3.Adım 3 (F3): Step 3 (Eq 20): Second-level standardization with direction. C_ij = R_ij for benefit; C_ij = −R_ij + max R + min R for cost. Formül: C_{ij} = R_{ij}\ (J^{+});\quad C_{ij} = -R_{ij} + \max_{i} R_{ij} + \min_{i} R_{ij}\ (J^{-}) Anchor: Kara et al. 2024, Eq (20)
4. 4.Adım 4 (F4): Step 4 (Eq 21): Ideal utility P+_ij = (C_ij/max_i C_ij) · W_j · β^u. Formül: P^{+}_{ij} = (C_{ij}/\max_{i} C_{ij}) \cdot W_{j} \cdot \beta^{u} Anchor: Kara et al. 2024, Eq (21)
5. 5.Adım 5 (F5): Step 5 (Eq 22): Anti-ideal utility P−_ij = (min_i C_ij/C_ij) · W_j · β^u. Formül: P^{-}_{ij} = (\min_{i} C_{ij}/C_{ij}) \cdot W_{j} \cdot \beta^{u} Anchor: Kara et al. 2024, Eq (22)
6. 6.Adım 6 (F6): Step 6 (Eqs 24-25): Aggregate utility N+ and N−. Formül: N^{+}_{i} = \sum_{j} P^{+}_{ij};\quad N^{-}_{i} = \sum_{j} P^{-}_{ij} Anchor: Kara et al. 2024, Eqs (24)-(25)
7. 7.Adım 7 (F7): Step 7 (Eq 26): Final utility K_i = (N+ + N−) + (ψ f(N+)^τ + (1−ψ) f(N−)^τ)^(1/τ); f(N+) = N+/(N+ + N−). Formül: K_{i} = (N^{+}_{i} + N^{-}_{i}) + (\psi (f(N^{+}_{i}))^{\tau} + (1-\psi)(f(N^{-}_{i}))^{\tau})^{1/\tau} Anchor: Kara et al. 2024, Eq (26)

Commonly paired with

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

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