Сравнение методов

	Робастное дискретно-событийное моделирование	Робастный сценарный анализ
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s–2000s	1950 (foundations); 2003 (modern RDM formulation)
Автор метода	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
Тип	Simulation with robustness analysis	Scenario-based robustness evaluation
Основополагающий источник	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127	Wald, A. (1950). Statistical Decision Functions. Wiley, New York.
Другие названия	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
Связанные	6	5
Сводка	Robust Discrete-Event Simulation (Robust DES) is a simulation methodology that extends classical discrete-event simulation by explicitly incorporating uncertainty in model parameters — such as interarrival times, service durations, and resource capacities — and evaluating system performance across worst-case or distributional uncertainty sets rather than point estimates alone. It is widely applied in manufacturing, healthcare, logistics, and supply chain systems where parameter misspecification or real-world variability can lead to misleading simulation conclusions.	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.