Сравнение методов

	Робастное линейное программирование	Робастное программирование целевых установок
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1999–2004	1961 (GP); 1990s (robust extension)
Автор метода	Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.	Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)
Тип	Uncertainty-robust linear optimization	Mathematical programming under uncertainty
Основополагающий источник	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041
Другие названия	RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming
Связанные	5	5
Сводка	Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited.	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.