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	Programmazione Lineare Stocastica	Programmazione Lineare Robusta
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1955	1999–2004
Ideatore	George B. Dantzig	Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.
Tipo	Stochastic optimization model	Uncertainty-robust linear optimization
Fonte seminale	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.	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Alias	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP	RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP
Correlati	5	5
Sintesi	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.	Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited.