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Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.

Metode, kas balstīta uz kritēriju noņemšanas efektiem×AROMAN×
NozareLēmumu pieņemšanaLēmumu pieņemšana
SaimeMCDMMCDM
Izcelsmes gads20212022
AutorsKeshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z.Zdravković, M., Hamid, M., Radovanović, M.
TipsRemoval-effect objective weighting (logarithmic utility)Two-step normalisation (linear + vector) with weighted power aggregation
PirmavotsKeshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Informatica DOI ↗Zdravković, M., Hamid, M., Radovanović, M. (2022). AROMAN — Alternative Ranking Order Method Accounting for Two-Step Normalisation. Journal of Computational Design and Engineering link ↗
Citi nosaukumi
Saistītās88
KopsavilkumsMEREC (MEthod based on the Removal Effects of Criteria) is a weight objective multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalisation) is a ranking multi-criteria decision-making (MCDM) method introduced by Zdravković, M., Hamid, M., Radovanović, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateSalīdzināt metodes: MEREC · AROMAN. Izgūts 2026-06-17 no https://scholargate.app/lv/compare