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Home›Decision-making›Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis
MCDMRankinghesitant

Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis

HF-MOORA (Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, Z.-H. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

HF-MOORA ranks alternatives based on performance scores. Higher score = better rank.

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
  • May exhibit rank reversal when alternatives are added to or removed from the set.
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Sources

  1. Li, Z.-H. (2014). An Extension of the MULTIMOORA Method for Multiple Criteria Group Decision Making Based upon Hesitant Fuzzy Sets. Journal of Applied Mathematics DOI: 10.1155/2014/527836 ↗

How to cite this page

ScholarGate. (2026, June 2). Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis. ScholarGate. https://scholargate.app/en/decision-making/hf-moora

Similar methods

HF-GRAHF-VIKORMOORAHF-COPRASHF-ARASMULTIMOORAROUGH-MOORAHF-ELECTRE-II

Related reference concepts

Decision MakingDecision Support SystemsWeighted ScoresCriteria for Decision-Making under Risk and UncertaintyDelphi TechniqueCost-Benefit Analysis

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

ScholarGate — HF-MOORA (Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/hf-moora · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Li, Z.-H.
Subfamily
Ranking
Year
2014
Type
Hesitant fuzzy ratio system ranker (benefit-vs-non-beneficial net score on vector-normalised defuzzified matrix)
Value Space
hesitant
Uncertainty
epistemic
Compensation
full
Rank Reversal
Yes
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