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लेक्सिकोग्राफ़िक गोल प्रोग्रामिंग×लक्ष्य प्रोग्रामिंग×लेक्सिकोग्राफ़िक बेस्ट वर्स्ट मेथड (Lexicographic Best Worst Method)×
क्षेत्रनिर्णयननिर्णयननिर्णयन
परिवारMCDMMCDMMCDM
उद्भव वर्ष196119552015
प्रवर्तकAbraham Charnes and William W. CooperCharnes, A., Cooper, W. W.Based on Rezaei's BWM framework and lexicographic optimization
प्रकारSequential goal optimization with priority levelsMulti-objective optimisation — weighted/lexicographic goal deviation minimisationSequential best-worst comparisons with priority hierarchy
मौलिक स्रोतCharnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91. DOI ↗Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985. DOI ↗
उपनामLexicographic GP, LGPLexicographic BWM
संबंधित284
सारांशLexicographic 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.
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