Compară metode

	Simularea Robustă a Cozilor	Model Markov Robust
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s–2018	2005
Autorul original	Whitt, W. and colleagues; Bertsimas, D. and colleagues	Nilim & El Ghaoui; Iyengar
Tip	Simulation with worst-case uncertainty propagation	Robust probabilistic model
Sursa seminală	Bertsimas, D., Natarajan, K., & Teo, C.-P. (2011). Distributionally robust optimization: A review. European Journal of Operational Research.	Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.
Denumiri alternative	RQS, Distributionally Robust Queueing, Robust Queue Simulation, Uncertainty-Aware Queueing Simulation	RMM, Robust Markov Chain, Uncertain Markov Model, Interval Markov Model
Înrudite	6	4
Rezumat	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.	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.