ScholarGate
Asistents

Salīdzināt metodes

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

Kubiskais-EDAS×Kritēriju korelācijas un standartnovirzes objektīvā svēršana×
NozareLēmumu pieņemšanaLēmumu pieņemšana
SaimeMCDMMCDM
Izcelsmes gads20232010
AutorsPaul, T.K., Jana, C., Pal, M.Wang, Y. M., Luo, Y.
TipsCubic Pythagorean Fuzzy ranking — CuPyFN = ⟨IvPyFN, PyFN⟩ = (⟨[Y⁻,Y⁺],[F⁻,F⁺]⟩,⟨Y,F⟩); Pythagorean constraint (Y⁺)²+(F⁺)² ≤ 1; average-solution EDAS with score-function PDA/NDACorrelation-penalised standard-deviation weighting
PirmavotsPaul, T.K., Jana, C., Pal, M. (2023). Multi-criteria group decision-making method in disposal of municipal solid waste based on cubic Pythagorean fuzzy EDAS approach with incomplete weight information. Applied Soft Computing DOI ↗Wang, Y. M., Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling DOI ↗
Citi nosaukumi
Saistītās88
KopsavilkumsCUBIC-EDAS (Cubic-EDAS — Cubic Pythagorean Fuzzy EDAS (CuP-EDAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Paul, T.K., Jana, C., Pal, M. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.CCSD (Criteria Correlation and Standard Deviation objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Wang, Y. M., Luo, Y. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ScholarGateDatu kopa
  1. v1
  2. 1 Avoti
  3. PUBLISHED
  1. v1
  2. 1 Avoti
  3. PUBLISHED

Doties uz meklēšanu Lejupielādēt slaidus

ScholarGateSalīdzināt metodes: CUBIC-EDAS · CCSD. Izgūts 2026-06-17 no https://scholargate.app/lv/compare