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| Symulacja Kolejkowa× | Model Markowa× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1909 | 1906 |
| Twórca≠ | Agner Krarup Erlang | Andrei Markov |
| Typ≠ | Stochastic simulation / analytical modeling | Probabilistic state-transition model |
| Źródło pierwotne≠ | Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York. ISBN: 978-0471491101 | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Inne nazwy | Queue Simulation, Queuing Theory Simulation, Waiting-Line Simulation, DES-Queue | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Queueing Simulation combines classical queueing theory with discrete-event simulation to model systems where entities arrive, wait for service, and depart. It predicts performance metrics such as average waiting time, queue length, and server utilization, enabling capacity planning and bottleneck identification across service, manufacturing, healthcare, and network systems. | 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. |
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