Сравнение методов

	Робастное дискретно-событийное моделирование	Стохастическое дискретно-событийное моделирование
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s–2000s	1960s–1970s
Автор метода	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community	Banks, Carson, Nelson, Nicol; Law, A. M.
Тип	Simulation with robustness analysis	Stochastic simulation 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	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127
Другие названия	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES
Связанные	6	6
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.	Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.