방법 비교

	불확실성 하에서의 대기열 성능 분석을 위한 강건한 대기열 시뮬레이션	심층 불확실성 하에서의 최악의 경우 및 최소 최대 후회 평가를 포함한 강건 시나리오 분석
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	2000s–2018	1950 (foundations); 2003 (modern RDM formulation)
창시자	Whitt, W. and colleagues; Bertsimas, D. and colleagues	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
유형	Simulation with worst-case uncertainty propagation	Scenario-based robustness evaluation
원전	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.
별칭	RQS, Distributionally Robust Queueing, Robust Queue Simulation, Uncertainty-Aware Queueing Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
관련	6	5
요약	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.