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Home›Decision-making›VIKOR with Stochastic Multicriteria Acceptability Analysis
MCDMRankingcrisp

VIKOR with Stochastic Multicriteria Acceptability Analysis

VIKOR-SMAA (VIKOR with Stochastic Multicriteria Acceptability Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Tervonen, T. Figueira, J. R. Lahdelma, R. Dias, J. A. Salminen, P. in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

VIKOR-SMAA is ideal when weights cannot be determined precisely. Output is probability of being preferred.

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. Tervonen, T., Figueira, J. R., Lahdelma, R., Dias, J. A., Salminen, P. (2009). A stochastic method for robustness analysis in sorting problems. European Journal of Operational Research DOI: 10.1016/j.ejor.2007.09.008 ↗

How to cite this page

ScholarGate. (2026, June 2). VIKOR with Stochastic Multicriteria Acceptability Analysis. ScholarGate. https://scholargate.app/en/decision-making/vikor-smaa

Similar methods

SMAA2SMAAVIKORFUZZY-VIKORHF-VIKORROUGH-VIKORPL-VIKORPIF-VIKOR

Related reference concepts

Decision MakingDecision Support SystemsCriteria for Decision-Making under Risk and UncertaintyDecision Making SkillsWeighted ScoresEvaluation Criteria

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

ScholarGate — VIKOR-SMAA (VIKOR with Stochastic Multicriteria Acceptability Analysis). Retrieved 2026-07-20 from https://scholargate.app/en/decision-making/vikor-smaa · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Tervonen, T. Figueira, J. R. Lahdelma, R. Dias, J. A. Salminen, P.
Subfamily
Ranking
Year
2009
Type
Monte Carlo acceptability indices from VIKOR under weight uncertainty
Value Space
crisp
Uncertainty
None
Compensation
partial
Rank Reversal
No
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