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Home›Decision-making›Neutrosophic MABAC
MCDMRankingSingle valued neutrosophic

Neutrosophic MABAC

N-MABAC (Neutrosophic MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Peng, Xindong Dai, Jingguo in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

Use N-MABAC when expert assessments involve truth (T), indeterminacy (I), and falsity (F) components.

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. Peng, Xindong, Dai, Jingguo (2018). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications DOI: 10.1007/s00521-016-2607-y ↗

How to cite this page

ScholarGate. (2026, June 2). Neutrosophic MABAC. ScholarGate. https://scholargate.app/en/decision-making/n-mabac

Similar methods

N-WASPASN-TODIMN-DNMAP-MABACN-MULTIMOORAN-VIKORN-PROMETHEEN-TOPSIS

Related reference concepts

Decision MakingDecision Support SystemsCriteria for Decision-Making under Risk and UncertaintyWeighted ScoresEvaluation CriteriaDecision Making Skills

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

ScholarGate — N-MABAC (Neutrosophic MABAC). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/n-mabac · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Peng, Xindong Dai, Jingguo
Subfamily
Ranking
Year
2018
Type
Single-valued neutrosophic extension of MABAC
Value Space
Single valued neutrosophic
Uncertainty
hybrid
Compensation
partial
Rank Reversal
No
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