Порівняння методів

	Надійна дискретно-подієва симуляція	Дискретно-подієве моделювання (DES)
Галузь	Імітаційне моделювання	Імітаційне моделювання
Родина	Process / pipeline	Process / pipeline
Рік появи	1990s–2000s	1960s (formalized); modern computational form from 1970s onward
Автор методу	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
Тип	Simulation with robustness analysis	Stochastic process simulation
Основоположне джерело	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.). Pearson. ISBN: 978-0136062127
Інші назви	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Пов'язані	6	4
Підсумок	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.	Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.