השוואת שיטות

	תכנון ליניארי סטוכסטי	תכנון דינמי סטוכסטי
תחום	סימולציה	סימולציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1955	1957
הוגה השיטה	George B. Dantzig	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
סוג	Stochastic optimization model	Sequential optimization under uncertainty
מקור מכונן	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.	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
כינויים	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP	SDP, Markov Decision Process, MDP, Stochastic DP
קשורות	5	6
תקציר	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.	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.