Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Diskrētā notikumu simulācija (DES) politikas scenāriji× | Monte Carlo simulācija× | |
|---|---|---|
| Nozare≠ | Simulācija | Lēmumu pieņemšana |
| Saime≠ | Process / pipeline | MCDM |
| Izcelsmes gads≠ | 1960s–1990s | 1949 |
| Autors≠ | Tocher, K. D. and Gordon, G. (early DES); policy scenario extension emerged through operations research and health policy modeling communities | Metropolis, N., Ulam, S. |
| Tips≠ | Simulation-based policy evaluation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Pirmavots≠ | Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education. ISBN: 9780073401324 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Citi nosaukumi≠ | Policy DES, Scenario-based DES, Policy simulation DES, DES policy analysis | — |
| Saistītās≠ | 5 | 0 |
| Kopsavilkums≠ | Policy Scenario Discrete-Event Simulation combines the event-by-event fidelity of Discrete-Event Simulation with systematic policy scenario analysis to evaluate how different interventions, regulations, or resource allocations change system performance. By running multiple well-defined policy scenarios through the same DES model, analysts can compare outcomes — throughput, waiting times, costs — across alternatives before real-world implementation. | 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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