Linganisha mbinu

	Njia Bora ya Mwisho ya Leksikografia	Njia ya Ubora wa Kinyama	Mpangilio Leksikografiki wa Malengo
Nyanja	Ufanyaji Maamuzi	Ufanyaji Maamuzi	Ufanyaji Maamuzi
Familia	MCDM	MCDM	MCDM
Mwaka wa asili	2015	2015 crisp; 2017 variant applicator	1961
Mwanzilishi	Based on Rezaei's BWM framework and lexicographic optimization	Guo, S., Zhao, H.	Abraham Charnes and William W. Cooper
Aina	Sequential best-worst comparisons with priority hierarchy	Triangular-fuzzy Best-to-Others and Others-to-Worst pairwise comparison with nonlinearly-constrained programming	Sequential goal optimization with priority levels
Chanzo asilia	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	Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91.
Majina mbadala	Lexicographic BWM	—	Lexicographic GP, LGP
Zinazohusiana	4	8	2
Muhtasari	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.	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.