Võrdle meetodeid

	Leksikograafiline parima-halvima meetod	Leksikograafiline eesmärgiprognoosimine	Stratifitseeritud parima-halvima meetod
Valdkond	Otsustamine	Otsustamine	Otsustamine
Perekond	MCDM	MCDM	MCDM
Tekkeaasta	2015	1961	2015
Looja	Based on Rezaei's BWM framework and lexicographic optimization	Abraham Charnes and William W. Cooper	Jafar Rezaei and collaborators
Tüüp	Sequential best-worst comparisons with priority hierarchy	Sequential goal optimization with priority levels	Hierarchical pairwise comparison with layer-wise best-worst
Algallikas	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.	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.
Rööpnimetused	Lexicographic BWM	Lexicographic GP, LGP	Stratified BWM
Seotud	4	2	4
Kokkuvõte	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.	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.