Methoden vergleichen

	Robuste ereignisdiskrete Simulation	Monte-Carlo-Simulation
Fachgebiet	Simulation	Entscheidungsfindung
Familie	Process / pipeline	MCDM
Entstehungsjahr	1990s–2000s	1949
Urheber	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community	Metropolis, N., Ulam, S.
Typ	Simulation with robustness analysis	Robustness wrapper — Monte Carlo uncertainty propagation
Wegweisende Quelle	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Aliasnamen	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation	—
Verwandt	6	0
Zusammenfassung	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.