Comparer des méthodes

	Simulation stochastique de files d'attente	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1953	1949
Auteur d'origine	Kendall, D. G.	Metropolis, N., Ulam, S.
Type	Stochastic simulation — waiting-line system analysis	Robustness wrapper — Monte Carlo uncertainty propagation
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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	—
Apparentées	6	0
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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.