MEREC: MEthod based on the Removal Effects of Criteria

Overview

Removal-effect objective weighting (logarithmic utility). Output typically weight (higher value = preferred).

Strengths

• •Method-specific: Removal-effect objective weighting (logarithmic utility)

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

• •when criterion j is removed.

Common pitfalls

• •Hatalı: 'MEREC bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Decision matrix exists with measurable criteria
• •Hatalı: 'MEREC bu varsayımı kontrol etmeden uygulamak'. Doğrusu: Sufficient inter-alternative variation per criterion
• •Hatalı: MEREC'yi 'No data variation (constant criterion) → weight degenerates' durumunda kullanmak: recommendation_metadata.not_recommended_when alternatif öneriyor.
• •Hatalı: MEREC'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: Logarithmic normalisation x'_ij ∈ [0,1]. Formül: x'_{ij} = \begin{cases} \dfrac{\min_{k} x_{kj}}{x_{ij}} & j\in J^{+} \\ \dfrac{x_{ij}}{\max_{k} x_{kj}} & j\in J^{-} \end{cases} Anchor: Keshavarz-Ghorabaee 2021, p.6 Eq.(2)
2. 2.Adım 2 (F2): Step 2: Overall performance S_i with all criteria. Formül: S_{i} = \ln\Big(1 + \big(\tfrac{1}{n} \sum_{j=1}^{n} |\ln(x'_{ij})|\big)\Big) Anchor: Keshavarz-Ghorabaee 2021, p.6 Eq.(3)
3. 3.Adım 3 (F3): Step 3: Performance S'_ij when criterion j is removed. Formül: S'_{ij} = \ln\Big(1 + \big(\tfrac{1}{n} \sum_{k\neq j} |\ln(x'_{ik})|\big)\Big) Anchor: Keshavarz-Ghorabaee 2021, p.6 Eq.(4)
4. 4.Adım 4 (F4): Step 4: Removal effect E_j = Σ |S_i − S'_ij|. Formül: E_{j} = \sum_{i=1}^{m} |S_{i} - S'_{ij}| Anchor: Keshavarz-Ghorabaee 2021, p.6 Eq.(5)
5. 5.Adım 5 (F5): Step 5: MEREC weights w_j = E_j / Σ E_k. Formül: w_{j} = \dfrac{E_{j}}{\sum_{k=1}^{n} E_{k}} Anchor: Keshavarz-Ghorabaee 2021, p.7 Eq.(6)

Commonly paired with

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

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