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Salīdzināt metodes

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

Normalizācija ar vektoriem×Novērtēšana, balstoties uz atšķirību no vidējā risinājuma×
NozareLēmumu pieņemšanaLēmumu pieņemšana
SaimeMCDMMCDM
Izcelsmes gads19812015
AutorsHwang, C. L., Yoon, K.Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z.
TipsNormalization (L2, unit-sphere projection)Distance from average solution
PirmavotsHwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag DOI ↗Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica DOI ↗
Citi nosaukumi
Saistītās48
KopsavilkumsNORM-VECTOR (Vector Normalization — Euclidean column-norm scaling (L2 normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.EDAS (Evaluation Based on Distance from Average Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateSalīdzināt metodes: NORM-VECTOR · EDAS. Izgūts 2026-06-17 no https://scholargate.app/lv/compare