Σύγκριση μεθόδων

	Μέθοδος Βέλτιστου-Χειρότερου Λεξικογραφικής Βελτιστοποίησης	Μέθοδος Καλύτερου-Χειρότερου	Στρωματοποιημένη Μέθοδος Βέλτιστου-Χειρότερου (Stratified Best Worst Method)
Πεδίο	Λήψη Αποφάσεων	Λήψη Αποφάσεων	Λήψη Αποφάσεων
Οικογένεια	MCDM	MCDM	MCDM
Έτος προέλευσης	2015	2015	2015
Δημιουργός	Based on Rezaei's BWM framework and lexicographic optimization	Rezaei, J.	Jafar Rezaei and collaborators
Τύπος	Sequential best-worst comparisons with priority hierarchy	Pairwise comparison (best-to-others + others-to-worst vectors), LP	Hierarchical pairwise comparison with layer-wise best-worst
Θεμελιώδης πηγή	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.	Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.
Εναλλακτικές ονομασίες	Lexicographic BWM	—	Stratified BWM
Συναφείς	4	8	4
Σύνοψη	Lexicographic BWM combines the strengths of the Best Worst Method with lexicographic (sequential) optimization. Instead of weighting all criteria simultaneously, it assigns criteria to priority levels, solves the BWM for the highest-priority criteria first, then solves for lower-priority criteria while keeping the higher-priority weights fixed. This ensures that higher-priority criteria are never sacrificed to improve lower-priority ones.	BWM (Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Rezaei, J. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	Stratified BWM is an extension of the Best Worst Method that applies the BWM logic recursively across multiple hierarchical layers. Instead of weighting criteria at a single level, it identifies the best and worst criterion within each level of a hierarchy, then aggregates weights across levels. This enables more realistic modeling of complex decision problems with natural hierarchical structures.