Порівняння методів

	Лексикографічне цільове програмування	Програмування цілей	Лексикографічний метод найкращого-найгіршого
Галузь	Прийняття рішень	Прийняття рішень	Прийняття рішень
Родина	MCDM	MCDM	MCDM
Рік появи	1961	1955	2015
Автор методу	Abraham Charnes and William W. Cooper	Charnes, A., Cooper, W. W.	Based on Rezaei's BWM framework and lexicographic optimization
Тип	Sequential goal optimization with priority levels	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Sequential best-worst comparisons with priority hierarchy
Основоположне джерело	Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91.	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.
Інші назви	Lexicographic GP, LGP	—	Lexicographic BWM
Пов'язані	2	8	4
Підсумок	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.	GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.