Vertaile menetelmiä

	GREY-MABAC	Analytic Hierarchy Process	ANP	Bayesian BWM
Tieteenala	Päätöksenteko	Päätöksenteko	Päätöksenteko	Päätöksenteko
Menetelmäperhe	MCDM	MCDM	MCDM	MCDM
Syntyvuosi	2015	1980	1996	2020
Kehittäjä	Pamucar, D., Cirovic, G.	Saaty, T. L.	Saaty, T. L.	Mohammadi, M., Rezaei, J.
Tyyppi	Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])	Pairwise comparison (eigenvalue)	Weight_Subjective (pairwise comparison, supermatrix, network structure)	Hierarchical Dirichlet posterior over weights via MCMC (JAGS) — group decision
Alkuperäislähde	Pamucar, D., Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications	Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York ISBN: 978-0070543713	Saaty, T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh ISBN: 0-9620317-9-8	Mohammadi, M., Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega
Rinnakkaisnimet	—	—	—	—
Liittyvät	8	8	8	8
Tiivistelmä	GREY-MABAC (Grey-MABAC — Grey extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Cirovic, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	AHP (Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	ANP (Analytic Network Process (AHP with feedback and interdependences)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	BWM-BAYESIAN (Bayesian BWM — Probabilistic Group Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Mohammadi, M., Rezaei, J. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.