So sánh phương pháp

	Mô phỏng Hàng đợi Mạnh mẽ	Mô phỏng Monte Carlo
Lĩnh vực	Mô phỏng	Ra quyết định
Họ	Process / pipeline	MCDM
Năm ra đời	2000s–2018	1949
Người khởi xướng	Whitt, W. and colleagues; Bertsimas, D. and colleagues	Metropolis, N., Ulam, S.
Loại	Simulation with worst-case uncertainty propagation	Robustness wrapper — Monte Carlo uncertainty propagation
Công trình gốc	Bertsimas, D., Natarajan, K., & Teo, C.-P. (2011). Distributionally robust optimization: A review. European Journal of Operational Research.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Tên gọi khác	RQS, Distributionally Robust Queueing, Robust Queue Simulation, Uncertainty-Aware Queueing Simulation	—
Liên quan	6	0
Tóm tắt	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.	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.