השוואת שיטות

	תכנון דינמי סטוכסטי	תכנון ליניארי סטוכסטי
תחום	סימולציה	סימולציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1957	1955
הוגה השיטה	Bellman, R.; formalized for stochastic settings by Puterman, M. L.	George B. Dantzig
סוג	Sequential optimization under uncertainty	Stochastic optimization model
מקור מכונן	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093	Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176.
כינויים	SDP, Markov Decision Process, MDP, Stochastic DP	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
קשורות	6	5
תקציר	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 Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world.