مقایسهٔ روشها
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| روش FUZZY-BWM (متد بهترین-بدترین فازی مثلثی)× | برنامهریزی هدف لکسیکوگرافیک× | روش بهترین-بدترین طبقهبندیشده× | |
|---|---|---|---|
| حوزه | تصمیمگیری | تصمیمگیری | تصمیمگیری |
| خانواده | MCDM | MCDM | MCDM |
| سال پیدایش≠ | 2015 crisp; 2017 variant applicator | 1961 | 2015 |
| پدیدآور≠ | Guo, S., Zhao, H. | Abraham Charnes and William W. Cooper | Jafar Rezaei and collaborators |
| نوع≠ | 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 |
| منبع بنیادین≠ | Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems DOI ↗ | Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91. 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 ↗ |
| نامهای دیگر≠ | — | Lexicographic GP, LGP | Stratified BWM |
| مرتبط≠ | 8 | 2 | 4 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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