Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовское целевое программирование× | Стохастическое целевое программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s | 1968 |
| Автор метода≠ | Rios Insua, D. and colleagues | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Тип≠ | Multi-objective optimization under uncertainty | Stochastic multi-goal optimization |
| Основополагающий источник≠ | Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814 | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ |
| Другие названия | BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Связанные | 6 | 6 |
| Сводка≠ | Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under uncertainty. | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. |
| ScholarGateНабор данных ↗ |
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