Compara mètodes

	Fuzzy BWM	Programació per Metes Lexicogràfica	Mètode Best Worst Estratificat
Camp	Presa de decisions	Presa de decisions	Presa de decisions
Família	MCDM	MCDM	MCDM
Any d'origen	2015 crisp; 2017 variant applicator	1961	2015
Autor original	Guo, S., Zhao, H.	Abraham Charnes and William W. Cooper	Jafar Rezaei and collaborators
Tipus	Triangular-fuzzy Best-to-Others and Others-to-Worst pairwise comparison with nonlinearly-constrained programming	Sequential goal optimization with priority levels	Hierarchical pairwise comparison with layer-wise best-worst
Font seminal	Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems	Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91.	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.
Àlies	—	Lexicographic GP, LGP	Stratified BWM
Relacionats	8	2	4
Resum	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.	Lexicographic Goal Programming (LGP) is a variant of goal programming introduced by Charnes and Cooper in the 1960s. It prioritizes multiple goals in a strict ordinal hierarchy, solving optimization problems sequentially: first achieve the highest-priority goal, then the second-highest while maintaining the first, and so on. This ensures that lower-priority goals are never pursued at the expense of higher-priority ones.	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.