Сравнение методов

	Программирование целей в сценарном анализе политики	Стохастическое целевое программирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1961 (goal programming); policy scenario application 1980s–present	1968
Автор метода	Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature	Contini, B. (building on Charnes & Cooper's chance-constrained programming)
Тип	Optimization under multiple conflicting goals across policy scenarios	Stochastic multi-goal optimization
Основополагающий источник	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405	Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.
Другие названия	PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming	SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming
Связанные	5	6
Сводка	Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space.	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.