Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Programmation Linéaire Stochastique× | Programmation par objectifs stochastique× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1955 | 1968 |
| Auteur d'origine≠ | George B. Dantzig | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Type≠ | Stochastic optimization model | Stochastic multi-goal 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. link ↗ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ |
| Alias | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Apparentées≠ | 5 | 6 |
| 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. | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. |
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