Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Programmation Linéaire Déterministe× | Programmation Linéaire Stochastique× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1947 | 1955 |
| Auteur d'origine | George B. Dantzig | George B. Dantzig |
| Type≠ | Deterministic mathematical optimization | 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. link ↗ |
| Alias | Classical LP, Deterministic LP, DLP, Linear Optimization | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP |
| Apparentées | 5 | 5 |
| Résumé≠ | Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data. | 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. |
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