方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 随机离散事件仿真× | 随机系统动力学× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1960s–1970s | 1980s–2000s |
| 提出者≠ | Banks, Carson, Nelson, Nicol; Law, A. M. | Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers |
| 类型≠ | Stochastic simulation model | Continuous stochastic simulation |
| 开创性文献≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 |
| 别名 | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES | SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios. |
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