FUCA: Flexible and Universal Compromise Analysis

Overview

Weighted rank aggregation (Borda-type with rank-based weights). Output typically utility (higher value = preferred).

Strengths

• •Method-specific: Weighted rank aggregation (Borda-type with rank-based weights)

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

• •Ties get average rank.

Common pitfalls

• •Hatalı: 'FUCA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Criteria preferences are independent (no synergistic interactions)
• •Hatalı: 'FUCA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Compensation is acceptable: high score on one criterion can offset low on another
• •Hatalı: 'FUCA bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix is complete (no missing values)
• •Hatalı: FUCA'yi 'Criteria strongly correlated → consider DEMATEL/ANP for interdependence' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: FUCA'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: Rank each alternative on each criterion (1 = best). For benefit: rank 1 = highest value. For cost: rank 1 = lowest value. Ties get average rank. Formül: \rho_{ij} = \text{rank of alternative } i \text{ on criterion } j \text{ (1=best, ties=average)} Anchor: Raveh 2000, p.672
2. 2.Adım 2 (F2): Step 2: Compute FUCA score F_i = weighted sum of per-criterion ranks. Rank ascending (lower = better). Formül: F_{i} = \sum_{j=1}^{n}w_{j}\,\rho_{ij} Anchor: Raveh 2000, p.672

Commonly paired with

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

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