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.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
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.
Sources
- Liao, H., Wu, X. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega DOI: 10.1016/j.omega.2019.04.001 ↗
How to cite this page
ScholarGate. (2026, June 2). Double Normalization-Based Multiple Aggregation. ScholarGate. https://scholargate.app/en/decision-making/dnma
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- AHPDecision-making↔ compare
- ANPDecision-making↔ compare
- BWMDecision-making↔ compare
- BWM-BAYESIANDecision-making↔ compare
- CCSDDecision-making↔ compare
- CILOSDecision-making↔ compare
- CIMASDecision-making↔ compare
- CRITICDecision-making↔ compare