Comparer des méthodes

	Simulation de Scénarios de Politiques de File d'Attente	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1909 (queueing theory); scenario application from 1960s–1970s OR literature	1906
Auteur d'origine	Erlang, A. K. (foundation); generalized by operations research community	Andrei Markov
Type	Comparative simulation experiment	Probabilistic state-transition model
Source fondatrice	Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York. ISBN: 978-0471491101	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	PSQS, policy queueing analysis, queueing policy comparison, scenario-based queueing model	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	5	5
Résumé	Policy Scenario Queueing Simulation applies queueing theory and discrete-event simulation to evaluate two or more competing service or resource-allocation policies under realistic demand and capacity conditions. By holding the system structure constant and varying only the policy rules, analysts can directly compare throughput, waiting times, utilization, and equity outcomes before committing to real-world implementation.	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.