Sammenlign metoder

	Stokastisk simuleringsmodellering av køsystemer	Markov-modell
Fagfelt	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1953	1906
Opphavsperson	Kendall, D. G.	Andrei Markov
Type	Stochastic simulation — waiting-line system analysis	Probabilistic state-transition model
Opprinnelig kilde	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
Alias	SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Relaterte	6	5
Sammendrag	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.