Сравнение методов

	Байесовское целевое программирование	Робастное программирование целевых установок
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s	1961 (GP); 1990s (robust extension)
Автор метода	Rios Insua, D. and colleagues	Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)
Тип	Multi-objective optimization under uncertainty	Mathematical programming under uncertainty
Основополагающий источник	Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041
Другие названия	BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming
Связанные	6	5
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.	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.