Salīdzināt metodes

	Stohastiskā rindošanas simulācija	Markov Model
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1953	1906
Autors	Kendall, D. G.	Andrei Markov
Tips	Stochastic simulation — waiting-line system analysis	Probabilistic state-transition model
Pirmavots	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.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Citi nosaukumi	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Saistītās	6	5
Kopsavilkums	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 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.