FARE: Factor Relationship weighting method

Overview

Weight_Subjective (pairwise factor relationship matrix, closed-form weights). Output typically weight (higher value = preferred).

Strengths

• •Method-specific: Weight_Subjective (pairwise factor relationship matrix, closed-form weights)

Limitations

• •Assumes: Decision matrix exists with measurable criteria
• •Assumes: Sufficient inter-alternative variation per criterion

Assumptions to verify

• •Decision matrix exists with measurable criteria
• •Sufficient inter-alternative variation per criterion

When not to use

• •No data variation (constant criterion) → weight degenerates
• •Expert judgment is the actual driver → use subjective weighting

Edge cases

• •See F.steps and D.parameters for FARE-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Hatalı: 'FARE bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix exists with measurable criteria
• •Hatalı: 'FARE bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Sufficient inter-alternative variation per criterion
• •Hatalı: FARE'yi 'No data variation (constant criterion) → weight degenerates' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: FARE'yi 'Expert judgment is the actual driver → use subjective weighting' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Step 1: Sum each row of the relation matrix to get S_i = Σ_j f_{ij}. Normalise: w_i = S_i / Σ_k S_k. Formül: S_i = \sum_{j=1}^{n} f_{ij};\quad w_i = \frac{S_i}{\sum_{k=1}^{n}S_k} Anchor: Žižović et al. 2020, p.1101 Eqs.(1)-(2) (pending PDF page verification)

Commonly paired with

• •FARE + TOPSIS (high)
• •FARE + VIKOR (high)
• •FARE + EDAS (high)
• •FARE + WASPAS (high)
• •FARE + MARCOS (high)

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