Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stohastiskā diskrēto notikumu simulācija× | Simulācija rindās× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1960s–1970s | 1909 |
| Autors≠ | Banks, Carson, Nelson, Nicol; Law, A. M. | Agner Krarup Erlang |
| Tips≠ | Stochastic simulation model | Stochastic simulation / analytical modeling |
| Pirmavots≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York. ISBN: 978-0471491101 |
| Citi nosaukumi | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES | Queue Simulation, Queuing Theory Simulation, Waiting-Line Simulation, DES-Queue |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. | 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. |
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