Comparer des méthodes

	Programmation linéaire bayésienne	Programmation Linéaire Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1970s–1980s	1955
Auteur d'origine	Integrated from Dantzig (LP) and Zellner/Bayesian econometrics traditions	George B. Dantzig
Type	Optimization under Bayesian uncertainty	Stochastic optimization model
Source fondatrice	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136	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.
Alias	BLP, Bayesian LP, Bayesian stochastic linear programming, prior-posterior LP	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Apparentées	6	5
Résumé	Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, BLP uses prior beliefs updated by data to form posterior distributions, which then guide the LP formulation and solution, producing decisions that are optimal in a probabilistic, data-informed sense.	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.