Сравнение методов

	Стохастическое целевое программирование	Робастное программирование целевых установок
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1968	1961 (GP); 1990s (robust extension)
Автор метода	Contini, B. (building on Charnes & Cooper's chance-constrained programming)	Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)
Тип	Stochastic multi-goal optimization	Mathematical programming under uncertainty
Основополагающий источник	Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041
Другие названия	SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming
Связанные	6	5
Сводка	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.	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.