Сравнение методов

	Робастное дискретно-событийное моделирование	Робастная модель Маркова
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s–2000s	2005
Автор метода	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community	Nilim & El Ghaoui; Iyengar
Тип	Simulation with robustness analysis	Robust probabilistic model
Основополагающий источник	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127	Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.
Другие названия	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation	RMM, Robust Markov Chain, Uncertain Markov Model, Interval Markov Model
Связанные	6	4
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.	A Robust Markov Model applies robustness principles to Markov chains by replacing single-point transition probabilities with uncertainty sets, then optimizing against the worst-case realization. Originally developed for robust Markov decision processes in operations research, it is used wherever transition rates are estimated with noise or are subject to adversarial variation, ensuring decisions remain safe across the full uncertainty range.