مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| برنامهریزی پویا تصادفی× | برنامهریزی عدد صحیح مختلط تصادفی× | |
|---|---|---|
| حوزه | شبیهسازی | شبیهسازی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1957 | 1990s–2000s |
| پدیدآور≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | Birge, J. R.; Louveaux, F.; Sen, S. |
| نوع≠ | Sequential optimization under uncertainty | Stochastic optimization model |
| منبع بنیادین≠ | 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 |
| نامهای دیگر | SDP, Markov Decision Process, MDP, Stochastic DP | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP |
| مرتبط≠ | 6 | 5 |
| خلاصه≠ | 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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