COOK-SEIFORD: Cook & Seiford (1978): distance-based ranking aggregation

Overview

Distance matrix + Hungarian assignment. Output typically rank_position (lower value = preferred).

Strengths

• •Method-specific: Distance matrix + Hungarian assignment
• •Native group-decision support (multi-DM aggregation built into the pipeline)

Limitations

• •Assumes: Input is a rank matrix (1=best, m=worst per voter)
• •Assumes: Each voter ranks all alternatives

Assumptions to verify

• •Input is a rank matrix (1=best, m=worst per voter)
• •Each voter ranks all alternatives

When not to use

• •Cardinal preferences important → use a MAUT method

Edge cases

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

Common pitfalls

• •Hatalı: 'COOK-SEIFORD bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Input is a rank matrix (1=best, m=worst per voter)
• •Hatalı: 'COOK-SEIFORD bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Each voter ranks all alternatives
• •Hatalı: COOK-SEIFORD'yi 'Cardinal preferences important → use a MAUT method' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.

Worked example

1. 1.Adım 1 (F1): Build m×m distance matrix where d_jk = sum over rankings of |rank(alt_j) − k|. Apply Hungarian algorithm to assign alternatives to target ranks minimizing total distance. Formül: d_{j,k} = Σ_i |r_ij − k|; solve assignment problem (min total distance) Anchor: Orakçı 2024 §1.8.7

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