Comparer des méthodes

	Simulation stochastique de files d'attente	Simulation stochastique à événements discrets
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1953	1960s–1970s
Auteur d'origine	Kendall, D. G.	Banks, Carson, Nelson, Nicol; Law, A. M.
Type	Stochastic simulation — waiting-line system analysis	Stochastic simulation model
Source fondatrice	Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127
Alias	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES
Apparentées	6	6
Résumé	Stochastic Queueing Simulation models waiting-line systems where arrival and service processes follow probability distributions rather than fixed rates. By simulating thousands of random events, it estimates performance measures — mean waiting time, queue length, server utilization — under realistic uncertainty, making it the standard tool for designing and evaluating service systems from hospitals to call centers.	Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.