Linganisha mbinu

	MABAC	Mchakato wa Mlolongo wa Kihakiki	ANP	Njia ya BWM ya Kibayesiani
Nyanja	Ufanyaji Maamuzi	Ufanyaji Maamuzi	Ufanyaji Maamuzi	Ufanyaji Maamuzi
Familia	MCDM	MCDM	MCDM	MCDM
Mwaka wa asili	2015	1980	1996	2020
Mwanzilishi	Pamučar, D., Ćirović, G.	Saaty, T. L.	Saaty, T. L.	Mohammadi, M., Rezaei, J.
Aina	Border approximation area (distance from BAA)	Pairwise comparison (eigenvalue)	Weight_Subjective (pairwise comparison, supermatrix, network structure)	Hierarchical Dirichlet posterior over weights via MCMC (JAGS) — group decision
Chanzo asilia	Pamučar, D., Ćirović, 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
Majina mbadala	—	—	—	—
Zinazohusiana	8	8	8	8
Muhtasari	MABAC (Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Ćirović, 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.