NMD: New Method of Determining objective criterion weights

Overview

Weight_Objective (normalised matrix column-mean based). Output typically weight (higher value = preferred).

Strengths

• •Method-specific: Weight_Objective (normalised matrix column-mean based)

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

Common pitfalls

• •Hatalı: 'NMD bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix exists with measurable criteria
• •Hatalı: 'NMD bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Sufficient inter-alternative variation per criterion
• •Hatalı: NMD'yi 'No data variation (constant criterion) → weight degenerates' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: NMD'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: Apply linear-max normalisation: benefit n_ij = x_ij/max; cost n_ij = min/x_ij. Formül: n_{ij}=x_{ij}/x_j^{\max}\text{(benefit)};\;n_{ij}=x_j^{\min}/x_{ij}\text{(cost)} Anchor: Bulut 2017, p.807 (pending PDF page verification)
2. 2.Adım 2 (F2): Step 2: Compute column mean μ_j of normalised matrix. Compute w_j = (1 − μ_j) / Σ_k (1 − μ_k). Higher weight for lower mean (more variation from 1 = best). Formül: \mu_j=\frac{1}{m}\sum_i n_{ij};\quad w_j=\frac{1-\mu_j}{\sum_k(1-\mu_k)} Anchor: Bulut 2017, p.807 Eq. (pending PDF page verification)

Commonly paired with

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

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