Linganisha mbinu

	Uigaji Imara wa Mistari ya Kungoja	Uchambuzi Imara wa Matukio
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2000s–2018	1950 (foundations); 2003 (modern RDM formulation)
Mwanzilishi	Whitt, W. and colleagues; Bertsimas, D. and colleagues	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
Aina	Simulation with worst-case uncertainty propagation	Scenario-based robustness evaluation
Chanzo asilia	Bertsimas, D., Natarajan, K., & Teo, C.-P. (2011). Distributionally robust optimization: A review. European Journal of Operational Research.	Wald, A. (1950). Statistical Decision Functions. Wiley, New York.
Majina mbadala	RQS, Distributionally Robust Queueing, Robust Queue Simulation, Uncertainty-Aware Queueing Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
Zinazohusiana	6	5
Muhtasari	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 Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty.