Double Normalization-Based Multiple Aggregation
DNMA (Double Normalization-Based Multiple Aggregation) is a ranking multi-criteria decision-making (MCDM) method introduced by Liao, H., Wu, X. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
U_i ∈ [0,1] approximately. Higher U means better. DNMA combines two normalisation paradigms — linear (min-max) and vector (Euclidean) — to reduce the influence of any single normalisation method on the ranking. λ=0.5 gives equal weight to both. λ=1 reduces to a min-max SAW; λ=0 gives a vector-normalised SAW.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Liao, H., Wu, X. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega
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Cite this page
ScholarGate. (2026, June 2). DNMA. ScholarGate. https://scholargate.app/decision-making/dnma