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| Stochastische Warteschlangensimulation× | Stochastische ereignisdiskrete Simulation× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1953 | 1960s–1970s |
| Urheber≠ | Kendall, D. G. | Banks, Carson, Nelson, Nicol; Law, A. M. |
| Typ≠ | Stochastic simulation — waiting-line system analysis | Stochastic simulation model |
| Wegweisende Quelle≠ | 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. DOI ↗ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| Aliasnamen | SQS, Probabilistic Queueing Simulation, Stochastic Queue Modeling, Random Queueing Simulation | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | 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. |
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