Сравнение методов

	Робастный сценарный анализ	Робастная оптимизация
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1950 (foundations); 2003 (modern RDM formulation)	1970s theoretical roots; modern tractable form from late 1990s–2004
Автор метода	Wald, A. (minimax foundation); Lempert et al. (RDM framework)	Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004)
Тип	Scenario-based robustness evaluation	Mathematical programming framework
Основополагающий источник	Wald, A. (1950). Statistical Decision Functions. Wiley, New York.	Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682
Другие названия	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis	minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization)
Связанные	5	5
Сводка	Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty.	Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data.