Сравнение методов

	Стохастическое моделирование очередей	Стохастическая марковская модель
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1953	1993
Автор метода	Kendall, D. G.	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Тип	Stochastic simulation — waiting-line system analysis	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Основополагающий источник	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.	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Другие названия	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Связанные	6	6
Сводка	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.	A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.