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Vector (L2) Normalization

OriginatorHwang, C. L. Yoon, K.Year1981Sources1

VECTOR-NORMALIZATION (Vector (L2) Normalization) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L. Yoon, K. in 1981. 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

Outputs normalised matrix. No ranking produced; downstream method handles ranking.

Strengths & limitations

Strengths
  • 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.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Sources

  1. 1.
    Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, Berlin
    DOI 10.1007/978-3-642-48318-9

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ScholarGate. (2026, June 2). VECTOR-NORMALIZATION. ScholarGate. https://scholargate.app/decision-making/vector-normalization

Vector (L2) Normalization | ScholarGate