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Home›Decision-making›Heronian Mean (HM)
MCDMAggregationcrisp

Heronian Mean (HM)

HERONIAN-MEAN (Heronian Mean (HM)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Yu, D. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

Higher HM score indicates better aggregated performance. Geometrically averages pairwise interactions between criteria.

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. Yu, D. (2012). Intuitionistic fuzzy geometric Heronian mean aggregation operators. Applied Soft Computing DOI: 10.1016/j.asoc.2012.09.021 ↗

How to cite this page

ScholarGate. (2026, June 2). Heronian Mean (HM). ScholarGate. https://scholargate.app/en/decision-making/heronian-mean

Similar methods

BONFERRONI-MEANPOWER-MEANWHMWAMDNMADHF-TOPSISPF-WASPASHF-VIKOR

Related reference concepts

Decision MakingWeighted ScoresDecision Support SystemsEvaluation CriteriaLinear Discriminant AnalysisHierarchical Cluster Analysis

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — HERONIAN-MEAN (Heronian Mean (HM)). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/heronian-mean · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Yu, D.
Subfamily
Aggregation
Year
2012
Type
Interrelationship-based aggregation — geometric pairwise interactions
Value Space
crisp
Uncertainty
None
Compensation
partial
Rank Reversal
No
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