Comparer des méthodes

	Programmation Linéaire en Nombres Entiers Stochastique	Programmation dynamique stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1955	1957
Auteur d'origine	Dantzig, G. B.; Beale, E. M. L.	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
Type	Optimization under uncertainty with discrete decisions	Sequential optimization under uncertainty
Source fondatrice	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
Alias	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	SDP, Markov Decision Process, MDP, Stochastic DP
Apparentées	6	6
Résumé	Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.