Comparer des méthodes

	Programmation Linéaire en Nombres Entiers Stochastique	Programmation Linéaire en Nombres Entiers
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1955	1958–1960
Auteur d'origine	Dantzig, G. B.; Beale, E. M. L.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Type	Optimization under uncertainty with discrete decisions	Mathematical optimization
Source fondatrice	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Alias	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
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.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.