পদ্ধতির তুলনা করুন

	Stochastic Scenario Analysis	Stochastic Linear Programming
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1955–1980s	1955
প্রবর্তক	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition	George B. Dantzig
ধরন	Probabilistic scenario enumeration and evaluation	Stochastic optimization model
মৌলিক উৎস	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.
অপর নাম	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
সম্পর্কিত	4	5
সারসংক্ষেপ	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.