Comparer des méthodes

	Programmation Linéaire Stochastique	Programmation Linéaire Robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1955	1999–2004
Auteur d'origine	George B. Dantzig	Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.
Type	Stochastic optimization model	Uncertainty-robust linear optimization
Source fondatrice	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
Apparentées	5	5
Résumé	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.