Comparer des méthodes

	Simulation de files d'attente	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1909	1906
Auteur d'origine	Agner Krarup Erlang	Andrei Markov
Type	Stochastic simulation / analytical modeling	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	Queue Simulation, Queuing Theory Simulation, Waiting-Line Simulation, DES-Queue	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	6	5
Résumé	Queueing Simulation combines classical queueing theory with discrete-event simulation to model systems where entities arrive, wait for service, and depart. It predicts performance metrics such as average waiting time, queue length, and server utilization, enabling capacity planning and bottleneck identification across service, manufacturing, healthcare, and network systems.	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.