השוואת שיטות

	ניתוח תרחישים סטוכסטי – הערכה הסתברותית מרובת תרחישים תחת אי-ודאות	תכנון דינמי סטוכסטי
תחום	סימולציה	סימולציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1955–1980s	1957
הוגה השיטה	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
סוג	Probabilistic scenario enumeration and evaluation	Sequential optimization under uncertainty
מקור מכונן	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
כינויים	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis	SDP, Markov Decision Process, MDP, Stochastic DP
קשורות	4	6
תקציר	Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.	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.