Min-Max Normalization — linear rescaling of each criterion column to [0, 1]
MIN-MAX-NORMALIZATION (Min-Max Normalization — linear rescaling of each criterion column to [0, 1]) 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
Min-max normalisation maps every criterion to [0,1] with 1 = best performer and 0 = worst performer in the current dataset. The result is dataset-dependent: adding or removing alternatives changes all normalised values. Use before methods that require [0,1] inputs (e.g. EDAS, CODAS, MARCOS).
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.
- Results depend on the chosen normalisation, weights, and parameter settings.
Common pitfalls
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Sources
- 1.Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag
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ScholarGate. (2026, June 2). MIN-MAX-NORMALIZATION. ScholarGate. https://scholargate.app/decision-making/min-max-normalization