Salīdzināt metodes

	Robustas rindu simulācija	Robust Discrete-Event Simulation
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2018	1990s–2000s
Autors	Whitt, W. and colleagues; Bertsimas, D. and colleagues	Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community
Tips	Simulation with worst-case uncertainty propagation	Simulation with robustness analysis
Pirmavots	Bertsimas, D., Natarajan, K., & Teo, C.-P. (2011). Distributionally robust optimization: A review. European Journal of Operational Research.	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127
Citi nosaukumi	RQS, Distributionally Robust Queueing, Robust Queue Simulation, Uncertainty-Aware Queueing Simulation	Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation
Saistītās	6	6
Kopsavilkums	Robust Queueing Simulation integrates robustness analysis into queueing system simulation by considering worst-case or uncertainty-set-driven scenarios for arrival rates, service distributions, and queue disciplines. It produces performance guarantees that hold across an entire family of plausible input distributions, making it essential for risk-sensitive service system design.	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.