Comparer des méthodes

	Programmation dynamique stochastique	Programmation stochastique à variables mixtes entières
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1957	1990s–2000s
Auteur d'origine	Bellman, R.; formalized for stochastic settings by Puterman, M. L.	Birge, J. R.; Louveaux, F.; Sen, S.
Type	Sequential optimization under uncertainty	Stochastic optimization model
Source fondatrice	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175
Alias	SDP, Markov Decision Process, MDP, Stochastic DP	SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP
Apparentées	6	5
Résumé	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.	Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints.