Methoden vergleichen

	Stochastische Szenarioanalyse	Stochastische Lineare Programmierung
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1955–1980s	1955
Urheber	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition	George B. Dantzig
Typ	Probabilistic scenario enumeration and evaluation	Stochastic optimization model
Wegweisende Quelle	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374	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.
Aliasnamen	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Verwandt	4	5
Zusammenfassung	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 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.