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	Metoda Leksykograficzna Najlepszy-Najgorszy	Rozmyta Metoda Najlepszego-Najgorszego (Fuzzy BWM	Uwarstwiona Metoda Najlepsza-Najgorsza (Stratified Best Worst Method)
Dziedzina	Podejmowanie decyzji	Podejmowanie decyzji	Podejmowanie decyzji
Rodzina	MCDM	MCDM	MCDM
Rok powstania	2015	2015 crisp; 2017 variant applicator	2015
Twórca	Based on Rezaei's BWM framework and lexicographic optimization	Guo, S., Zhao, H.	Jafar Rezaei and collaborators
Typ	Sequential best-worst comparisons with priority hierarchy	Triangular-fuzzy Best-to-Others and Others-to-Worst pairwise comparison with nonlinearly-constrained programming	Hierarchical pairwise comparison with layer-wise best-worst
Źródło pierwotne	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.	Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.
Inne nazwy	Lexicographic BWM	—	Stratified BWM
Pokrewne	4	8	4
Podsumowanie	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.	FUZZY-BWM (Fuzzy BWM — Triangular Fuzzy Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Guo, S., Zhao, H. in 2015 crisp; 2017 variant applicator. 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.