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البرمجة الديناميكية العشوائية×محاكاة مونت كارلو×
المجالالمحاكاةاتخاذ القرار
العائلةProcess / pipelineMCDM
سنة النشأة19571949
صاحب الطريقةBellman, R.; formalized for stochastic settings by Puterman, M. L.Metropolis, N., Ulam, S.
النوعSequential optimization under uncertaintyRobustness wrapper — Monte Carlo uncertainty propagation
المصدر التأسيسيBellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
الأسماء البديلةSDP, Markov Decision Process, MDP, Stochastic DP
ذات صلة60
الملخصStochastic 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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ScholarGateقارن الطرق: Stochastic Dynamic Programming · MONTE-CARLO-SIMULATION. استُرجع بتاريخ 2026-06-15 من https://scholargate.app/ar/compare