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| Programowanie stochastyczne dynamiczne× | Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1957 | 1990s–2000s |
| Twórca≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | Birge, J. R.; Louveaux, F.; Sen, S. |
| Typ≠ | Sequential optimization under uncertainty | Stochastic optimization model |
| Źródło pierwotne≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 |
| Inne nazwy | SDP, Markov Decision Process, MDP, Stochastic DP | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints. |
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