Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stohastiskā diskrēto notikumu simulācija× | Monte Carlo simulācija× | |
|---|---|---|
| Nozare≠ | Simulācija | Lēmumu pieņemšana |
| Saime≠ | Process / pipeline | MCDM |
| Izcelsmes gads≠ | 1960s–1970s | 1949 |
| Autors≠ | Banks, Carson, Nelson, Nicol; Law, A. M. | Metropolis, N., Ulam, S. |
| Tips≠ | Stochastic simulation model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Pirmavots≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Citi nosaukumi≠ | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES | — |
| Saistītās≠ | 6 | 0 |
| 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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