Сравнение методов

	Робастное программирование целевых установок	Стохастическое целевое программирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1961 (GP); 1990s (robust extension)	1968
Автор метода	Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)	Contini, B. (building on Charnes & Cooper's chance-constrained programming)
Тип	Mathematical programming under uncertainty	Stochastic multi-goal optimization
Основополагающий источник	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041	Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.
Другие названия	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming	SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming
Связанные	5	6
Сводка	Robust Goal Programming (RGP) extends classical goal programming to handle uncertain or ambiguous model parameters. Instead of minimizing deviations from crisp targets, it seeks solutions that remain feasible and near-optimal across a range of plausible scenarios or uncertain data realizations. RGP is particularly valuable in planning problems where goals are aspirational and input data carries inherent variability or estimation error.	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.