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| Stochastic Dynamic Programming× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 1957 | 1949 |
| Urheber≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | Metropolis, N., Ulam, S. |
| Typ≠ | Sequential optimization under uncertainty | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | SDP, Markov Decision Process, MDP, Stochastic DP | — |
| Verwandt≠ | 6 | 0 |
| Zusammenfassung≠ | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. | 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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