ScholarGate
Asistents

Salīdzināt metodes

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

Adaptīva kaimiņattiecību sakārtotā svērtā vidējā vērtība×Metode, kas balstīta uz kritēriju noņemšanas efektiem×
NozareLēmumu pieņemšanaLēmumu pieņemšana
SaimeMCDMMCDM
Izcelsmes gads20142021
AutorsMalczewski, J.; Liu, X.Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z.
TipsRange-sensitive neighbourhood-local OWA — criterion weights w^q_k scale with local criterion variance within each spatial neighbourhood; order weights λ_k remain global, encoding a single risk attitude applied everywhereRemoval-effect objective weighting (logarithmic utility)
PirmavotsMalczewski, J., Liu, X. (2014). Local ordered weighted averaging in GIS-based multicriteria analysis. Annals of GIS DOI ↗Keshavarz 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 ↗
Citi nosaukumi
Saistītās88
KopsavilkumsLOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J.; Liu, X. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.MEREC (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.
ScholarGateDatu kopa
  1. v1
  2. 1 Avoti
  3. PUBLISHED
  1. v1
  2. 1 Avoti
  3. PUBLISHED

Doties uz meklēšanu Lejupielādēt slaidus

ScholarGateSalīdzināt metodes: LOCAL-OWA · MEREC. Izgūts 2026-06-18 no https://scholargate.app/lv/compare