MEREC-G
Method Based on the Removal Effects of Criteria - Generalized (MEREC-G) · Also known as: MEREC-G, Generalized MEREC
MEREC-G (Method Based on Removal Effects of Criteria - Generalized) is an objective weight derivation method that assigns weights based on the impact of removing each criterion from the decision analysis. The core idea is that important criteria, when removed, cause large changes in the final ranking. Generalized variants extend the original MEREC to various aggregation logic and decision contexts.
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When to use it
Use MEREC-G when you want objective, data-driven weights that reflect criterion importance based on decision impact. It is ideal for situations where data is available and expert judgment is less reliable. Prefer it over subjective methods when consistency and reproducibility are critical.
Strengths & limitations
- Fully objective; no subjective expert input or preferences required
- Highly interpretable; weight derivation is transparent and intuitive
- Captures cumulative effect on ranking; important criteria are weighted by their actual decision impact
- Robust to irrelevant alternatives; adding or removing non-critical alternatives does not significantly change weights
- Data-driven justification; weights are grounded in the actual decision matrix
- Sensitive to initial aggregation method; different aggregation formulas yield different removal impacts
- Assumes that impact (change in ranking) reflects importance; in some domains, this assumption may not hold
- Does not capture stakeholder preferences; purely data-driven approach ignores decision-maker values
- Computational cost grows linearly with number of criteria; each criterion removal requires full recalculation
Frequently asked
Why use MEREC-G instead of CRITIC-M?
MEREC-G measures removal impact on ranking; CRITIC-M measures variance and correlation. They capture different aspects of criterion importance. Use MEREC-G if removal impact is what you care about; use CRITIC-M if variance/distinctiveness matters more. Compare both approaches.
How sensitive are MEREC-G weights to normalization method?
Very sensitive. Try multiple normalization schemes (vector, linear, min-max, z-score) and compare resulting weights. If weights differ significantly, report sensitivity and use the most defensible normalization.
Can I use MEREC-G with subjective preference weights?
No. MEREC-G is objective by design. If you want to incorporate preferences, hybrid methods (combine MEREC-G weights with expert weights) are an option, but this loses the objectivity advantage.
Sources
- Keshavarz Ghorabaee, M., Hosseinzadeh Lotfi, F., Behzadi, M., & Sałabun, W. (2021). MEREC: A new multi-criteria model to evaluate wind farm locations. Sustainability, 12(15), 6136. link ↗
- Pamučar, D., Ćirović, G., & Božanović-Kečan, S. (2021). A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry, 12(9), 1549. link ↗
How to cite this page
ScholarGate. (2026, June 3). Method Based on the Removal Effects of Criteria - Generalized (MEREC-G). ScholarGate. https://scholargate.app/en/decision-making/merec-g
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